Exploring Linguistic Intuition in Large Linguistic Models

Exploring Linguistic Intuition in Large
Linguistic Models
Roman V. Dushkin
Abstract: This paper explores the concept of linguistic intuition in large linguistic
models and investigates the potential substructures formed within these models during
training that enable them to solve complex linguistic problems. The study focuses on
three distinct languages: Rumbaka, Numlang, and Silvorian, which possess unique
characteristics in terms of alphabet, sentence structure, and word order. A training
dataset comprising carefully designed sentences covering all vocabulary and grammar
aspects of the languages was used to train the large linguistic models, followed by
evaluation on a diverse test dataset containing correct sentences as well as sentences
with inconsistent tenses, unknown vocabulary, and unknown grammar. The main
hypothesis of this research is that large linguistic models possess a form of linguistic
intuition that allows them to effectively process and solve linguistic problems, even
those requiring algorithmic approaches. While it is recognized that large linguistic
models lack explicit symbolic problem-solving abilities, the investigation focuses on the
potential emergence of substructures within their neural connections that enable them to
exhibit linguistic intuition. The research questions aim to assess the linguistic intuition
of large linguistic models by analyzing their performance on the test dataset, comparing
their responses to correct answers, and analyzing their handling of different types of
incorrect sentences. The objectives are to evaluate the capabilities of large linguistic
models in terms of language understanding and translation, understand the impact of
substructures on their performance, and contribute to the development of more
advanced language models. The significance of this study lies in its potential to advance
large linguistic models capabilities, inform model design, improve natural language
processing applications, and enhance our understanding of language processing. By
exploring the presence of linguistic intuition in large linguistic models, scientists can
unlock their potential in solving complex language-related problems and bridge the gap
between machine learning and linguistic theory.
Keywords: large linguistic models, linguistic intuition, language processing,
substructures, natural language processing, machine learning, language understanding,
translation, symbol processing.
1. Introduction
Language is a complex and dynamic system that has long fascinated researchers
and linguists. With the advent of large linguistic models (LLMs) [], such as OpenAI
ChatGPT [], OpenAI GPT-4 [], Google Bard [], Stanford Alpaca [], and Meta
LLaMA [], there has been a significant leap in the ability to process linguistic
information []. These models, based on advanced neural network architectures [],
have demonstrated remarkable capabilities in understanding and generating
human-like text []. However, the question of whether LLM possesses some form of
«linguistic intuition» [] remains an intriguing and unexplored area of research.
The motivation behind this study stems from the desire to investigate the
underlying mechanisms that enable LLMs to excel in solving linguistic problems.
While LLMs are primarily statistical models [], there is a hypothesis that during their
training process, substructures may emerge within the vast network of connections
between neurons. These substructures could potentially provide the models with a
form of «linguistic intuition», allowing them to navigate linguistic tasks effectively,
despite the absence of explicit symbolic problem-solving algorithms [].
It is important to note that there is a clear understanding that LLMs, as statistical
models, lack inherent symbolic problem-solving capabilities []. Nevertheless, by
examining the spontaneous emergence of substructures in their link structures, this
study aims to indirectly investigate the existence of linguistic intuition within these
models. This research seeks to shed light on the nature of this intuition and
understand its potential impact on LLMs' performance in processing linguistic
information.
By comparing the linguistic intuition of ChatGPT, GPT-4, Bard, Alpaca, and
LLaMA, this study aims to contribute to understanding of the inner workings of
LLMs and their capacity to handle various linguistic tasks. Through an analysis of
their responses to specific linguistic problems, this work will explore whether these
models demonstrate a level of intuition that enables them to tackle complex
language-related challenges.
The findings from this research have the potential to unlock new insights into
the capabilities of LLMs and pave the way for advancements in natural language
processing and artificial intelligence. Understanding the mechanisms underlying
linguistic intuition in LLMs could have implications for improving language
generation, machine translation, and other language-based applications. Ultimately,
this study aims to broaden the understanding of the relationship between neural
network architectures and linguistic processing, leading to enhanced models that
better mimic human-like language abilities.
Research Questions:
1. To what extent do LLMs demonstrate linguistic intuition in their responses to
various language-related tasks?
2. What substructures, if any, can be identified within the link structures of LLMs that
may contribute to their linguistic intuition?
3. How does the presence of linguistic intuition in LLMs affect their performance in
solving linguistic problems, particularly in the context of unknown vocabulary and
grammar?
Objectives of this research:
1. Examine the linguistic intuition exhibited by LLMs in their responses to different
linguistic tasks, including sentence translation and comprehension.
2. Investigate the impact of linguistic intuition on LLMs' ability to handle challenges
related to unknown vocabulary and grammar.
3. Compare and contrast the performance of ChatGPT, GPT-4, Bard, Alpaca, and
LLaMA in terms of linguistic intuition, identifying similarities and differences
among the models.
4. Explore the implications of the findings for the field of natural language processing
and the development of more advanced language models.
By addressing these research questions and objectives, this study aims to
provide valuable insights into the phenomenon of linguistic intuition in LLMs,
advancing our understanding of their capabilities and potential for language
processing tasks.
This study on exploring linguistic intuition in LLMs holds significant
implications for the field of natural language processing and the development of
advanced language models. The findings of this research will contribute to a deeper
understanding of the abilities and limitations of LLMs in processing linguistic
information, shedding light on their potential to solve complex language-related
problems.
1. Advancing the Understanding of Linguistic Intuition. By investigating the presence
of linguistic intuition in LLMs, this study aims to uncover the underlying
mechanisms and substructures within the models that contribute to their ability to
process language. This understanding can pave the way for the development of
more sophisticated LLM architectures that harness and enhance linguistic intuition.
2. Enhancing Language Model Performance. Through the analysis of LLMs'
responses to various linguistic tasks, including translation and comprehension, this
research will provide insights into the impact of linguistic intuition on their overall
performance. Understanding how linguistic intuition influences LLMs' handling of
unknown vocabulary and grammar can guide the development of strategies to
improve their accuracy and adaptability.
3. Informing Model Design and Development. The identification of substructures
within LLM interneuron link structures that may be responsible for linguistic
intuition can inform future model design and development efforts. By studying
these substructures, researchers can devise techniques to enhance the emergence
and utilization of linguistic intuition in LLMs, leading to more robust and efficient
language models.
4. Advancing Natural Language Processing Applications. The findings of this study
have practical implications for various natural language processing (NLP)
applications, including machine translation, information retrieval, and chatbot
systems. Improved understanding of linguistic intuition in LLMs can facilitate the
development of more sophisticated and contextually aware language processing
systems, enabling more accurate and nuanced interactions between machines and
human users.
5. Contributing to Linguistic Theory. The investigation of linguistic intuition in
LLMs can provide valuable insights into the nature of human language processing.
By analyzing the correlations between LLMs' linguistic intuition and linguistic
phenomena, this study can contribute to the broader field of linguistics, deepening
our understanding of language structure, syntax, and semantics.
The significance of this study lies in its potential to advance the capabilities of
LLMs, inform model design and development, improve natural language processing
applications, and contribute to our understanding of language processing. By
investigating the presence of linguistic intuition in LLMs and examining the
substructures within these models, this study can enhance their performance and
harness their potential in solving complex language-related problems.
This research not only has practical implications for machine translation [],
information retrieval [], and chatbot systems [] but also contributes to the broader
field of linguistics by deepening our understanding of language structure and
semantics. Ultimately, this study bridges the gap between machine learning and
linguistic theory [], paving the way for more sophisticated and contextually aware
language models.
2. Review
LLMs have revolutionized the field of NLP by demonstrating remarkable
capabilities in understanding and generating human-like text []. These models, built
upon the advancements in deep learning [] and artificial neural networks, have shown
exceptional proficiency in various language-related tasks such as language translation
[], sentiment analysis [], question answering [], and text generation [].
LLMs are designed to learn patterns, structures, and semantics of language by
processing vast amounts of text data []. They utilize sophisticated algorithms and
artificial neural network architectures to encode and decode linguistic information,
enabling them to generate coherent and contextually appropriate responses. These
models excel at capturing complex syntactic and semantic relationships, allowing
them to comprehend and generate text with impressive fluency and accuracy [].
The power of LLMs stems from their ability to capture and learn from the vast
linguistic knowledge embedded in their training data []. They can recognize and
generalize linguistic patterns, understand contextual cues, and infer implicit
information, making them valuable tools for various language-based applications [].
These models leverage the vastness and diversity of training data to extract
meaningful representations of language, enabling them to exhibit a form of language
understanding that was previously thought to be exclusive to humans [].
LLMs typically employ deep artificial neural network architectures, such as
transformer models [], which consist of multiple layers of self-attention mechanisms
[]. These architectures allow the models to efficiently process and analyze the
relationships between different words, phrases, and sentences within a given context.
By leveraging this contextual information, LLMs can generate highly coherent and
contextually appropriate responses.
Despite their impressive capabilities, it is important to recognize that LLMs are
statistical models and lack explicit symbolic reasoning abilities []. While they excel at
pattern recognition and statistical inference, their understanding of language remains
grounded in statistical associations rather than explicit rule-based reasoning []. This
research tries to show that LLMs may exhibit a form of «linguistic intuition» that
enables them to solve linguistic problems in ways that resemble human language
processing.
In this study, one can delve deeper into the linguistic intuition of LLMs and
investigate the presence of substructures within their neural connections that may
contribute to their language processing abilities. By training LLMs on distinct
languages with diverse linguistic characteristics, the study aims to uncover any
emergent substructures that facilitate their performance in linguistic tasks. Through a
carefully designed evaluation process, one could analyze the ability of LLMs to
handle correct sentences as well as sentences with inconsistent tenses, unknown
vocabulary, and unknown grammar.
The LLMs investigated in this research include OpenAI ChatGPT, OpenAI
GPT-4, Google Bard, Stanford Alpaca, and Meta LLaMA:
● ChatGPT is a widely used large linguistic model developed by OpenAI. It is based
on the GPT-3.5 architecture and has been trained on a vast amount of text data to
generate coherent and contextually appropriate responses in natural language
conversations [].
● GPT-4, the successor to ChatGPT, represents the next iteration of the GPT series.
With advancements in architecture and training techniques, GPT-4 aims to further
enhance the language processing capabilities of LLMs [].
● Google Bard is a large linguistic model developed by Google Research. It is based
on the LaMDA family of LLMs and later the PaLM LLM and utilizes
state-of-the-art techniques in deep learning and language modeling to understand
and generate text in a manner that closely resembles human language [].
● Alpaca is an LLM developed by researchers at Stanford University. It was derived
from LLaMA LLM by fine-tubing to improve language understanding and
generation tasks [].
● LLaMA is a cutting-edge large linguistic model developed by Meta AI. It
incorporates novel techniques and approaches to language modeling, leveraging
extensive training data to achieve high levels of linguistic proficiency. Often it is
the base for fine-tuning of other LLMs to achieve goals in specific and particular
domains [].
These LLMs have been selected for this research due to their widespread usage,
availability, and proven track record in NLP tasks. By examining and comparing their
performance on various linguistic tasks, this study aims to gain insights into their
linguistic intuition and shed light on their underlying mechanisms of language
processing.
Research on language intuition has made significant contributions to the
understanding of how humans process and comprehend language. One notable study
in this field is Steven Pinker's seminal work «Language as Instinct» (1994) [], which
presents compelling arguments suggesting that the human brain could possess innate
structures that enable the perception and parsing of language sequences, including
syntactic language structures.
Pinker's research challenges the notion that language is solely a product of
cultural or environmental influences. Instead, he posits that our ability to acquire and
use language is deeply rooted in our biology. According to Pinker, the natural neural
networks in the human brain may have genetically determined structures that
facilitate language processing. These structures enable humans to effortlessly grasp
the complex rules and patterns that govern language, such as grammar and syntax.
By examining extensive empirical evidence from various linguistic phenomena,
Pinker demonstrates that language acquisition occurs effortlessly and rapidly in
children, suggesting the presence of innate language faculties. He argues that these
innate linguistic abilities are shaped by evolution, allowing humans to communicate
and comprehend language with relative ease.
Stanislas Dehaene, in his influential book «Reading in the Brain» (2009) [],
arrives at similar conclusions regarding the existence of innate language processing
mechanisms in the human brain. Dehaene explores the neural underpinnings of
reading and demonstrates that the brain has specialized regions dedicated to
processing spoken and written language. He suggests that these neural networks are
not acquired through explicit instruction but are instead pre-existing, allowing for the
rapid development of reading skills in individuals.
The research conducted by Pinker and Dehaene aligns with the hypothesis
underlying this study, which proposes that LLMs may exhibit a form of «linguistic
intuition» due to the formation of substructures within their neural connections during
training. These substructures, analogous to the genetically determined structures in
the human brain, could enable LLMs to process linguistic information and solve
linguistic problems effectively.
The notion of «linguistic intuition» in LLMs is based on the understanding that
during their training process, substructures may emerge within the intricate network
of neural connections. These substructures are not explicitly programmed but rather
spontaneously develop as the models process vast amounts of linguistic data. It is
hypothesized that these substructures act as a form of internal representation,
allowing LLMs to capture and leverage the underlying patterns and structures of
language.
Similar to the genetically determined structures in the human brain proposed by
Pinker and Dehaene, these substructures in LLMs are believed to play a crucial role
in their ability to process linguistic information. The formation of such substructures
can be seen as an emergent property of the training process, where the models learn
to recognize and generalize linguistic patterns and regularities.
The potential existence of linguistic intuition in LLMs has significant
implications for NLP and model development. If LLMs exhibit linguistic intuition, it
suggests that they possess a level of understanding and proficiency in language that
extends beyond statistical patterns. This understanding can pave the way for
improved language generation, translation, and comprehension tasks, as well as the
development of more advanced language models.
Exploring the presence and characteristics of linguistic intuition in LLMs not
only expands the knowledge of language processing but also informs the design and
optimization of these models. By studying the substructures that emerge within
LLMs' neural connections, researchers can gain insights into the underlying
mechanisms that enable them to process language effectively. Such insights can guide
the development of more efficient architectures, training methodologies, and
strategies for fine-tuning LLMs [].
In addition to its implications for NLP and model development, the investigation
of linguistic intuition in LLMs holds great significance in the realm of machine
learning and linguistic theory. It serves as a bridge between the statistical
methodologies commonly employed in machine learning and the symbolic nature of
language.
Traditionally, machine learning approaches have heavily relied on statistical
patterns and correlations to process and generate language []. However, the symbolic
aspects of language, such as syntax and semantics, have posed challenges for purely
statistical models []. By delving into the intuitive abilities of LLMs, one can uncover
a deeper understanding of how these models effectively navigate the intricacies of
language.
The exploration of linguistic intuition in LLMs provides valuable insights into
the fundamental properties of language and offers an opportunity to refine linguistic
theories. By studying the substructures that emerge within LLMs' neural connections,
one can gain empirical evidence to inform and enhance linguistic theories. This
empirical approach allows to augment existing theories with concrete observations of
how language is processed, leading to a more comprehensive understanding of
linguistic phenomena.
Additionally, understanding the intuitive abilities of LLMs contributes to the
ongoing dialogue between machine learning and linguistic theory. It facilitates the
integration of symbolic aspects into statistical models and encourages a more holistic
approach to NLP. This interdisciplinary collaboration fosters the development of
more sophisticated language models and opens avenues for further research at the
intersection of machine learning and linguistics.
3. Methodology
In this section a novel approach to investigate linguistic intuition in LLMs is
adopted. Recognizing the need to develop new languages to ensure the absence of
prior exposure to LLMs during training, three distinct languages were designed and
constructed, each encompassing different linguistic properties. This allowed us to
explore various possibilities and manifestations of language intuition in LLMs.
The selection of the languages was based on three key properties: word order,
language type, and alphabet type. Word order was chosen as a fundamental
characteristic that influences the structure and organization of sentences. This
research specifically opted for three different word orders: Subject-Object-Verb
(SOV), Subject-Verb-Object (SVO), and Verb-Subject-Object (VSO) []. By
incorporating these varied word orders, it was aimed to capture the potential
influence of word order on language intuition in LLMs.
To account for different language types, it was considered the degree of
synthesis or fusion in morphological structure. We chose to represent isolating,
agglutinative, and flexive language types. Isolating languages predominantly utilize
separate words or morphemes to convey meaning [], whereas agglutinative languages
employ affixes to indicate various grammatical information []. Flexive languages
demonstrate a fusion of grammatical categories within a single word []. By
incorporating these diverse language types, we aimed to assess the impact of
morphological complexity on LLMs' ability to exhibit linguistic intuition.
Furthermore, we incorporated variations in alphabet types to explore the
influence of script on language intuition. The selected alphabet types included Latin,
Cyrillic, and an abstract alphabet composed of numbers. By employing different
scripts, we sought to determine whether the symbolic representation of language
influenced the development of language intuition in LLMs.
To create the dataset for training and testing, we developed a lexicon for each of
the three languages and constructed grammars that encapsulated the syntactic rules
and structures of the respective language types. The dataset comprised 25 carefully
crafted sentences that covered the entire vocabulary and grammar of the languages.
These sentences were designed based on the principle of one-shot learning, ensuring
comprehensive coverage of linguistic phenomena.
By intentionally constructing new languages with distinct word orders, language
types, and alphabet types, we aimed to provide a controlled environment for assessing
the presence and nature of language intuition in LLMs. This approach allowed us to
explore the unique capabilities of LLMs and investigate how they processed
linguistic information and solved language-related problems.
The vocabulary and grammar of each language are presented in Appendix A for
reference and detailed examination.
The training process employed a one-shot learning approach to train the LLMs
in the study. Each LLM was initially provided with a training dataset consisting of 25
sentences from one of the constructed languages. These sentences were accompanied
by their corresponding English translations, serving as the reference for learning the
language's vocabulary and grammar. The LLMs were then subjected to a series of
language-specific queries and translation tasks, involving sentences not present in the
training dataset.
The test dataset utilized in this study was carefully constructed to evaluate the
LLMs' performance on various linguistic challenges. It consisted of two distinct sets
of sentences: one for translation from the unknown language to English and another
for translation from English to the unknown language. Each set comprised a total of 5
sentences, ensuring comprehensive coverage of the language's vocabulary and
grammar.
The test dataset was designed to assess the LLMs' ability to handle different
linguistic scenarios. It included five categories of sentences for evaluation:
1. Two sets of correct sentences. This category contained five sentences that were
grammatically and semantically correct in both the unknown language and English.
These sentences served as a benchmark for assessing the LLMs' accuracy in
capturing the intended meaning and translating accurately.
2. Inconsistent tenses. Five sentences in this category presented challenges related to
tense consistency. The LLMs were evaluated based on their ability to detect and
handle inconsistencies in tense usage while maintaining coherent translations.
3. Unknown vocabulary (one word). This category included five sentences with one
unknown word, introducing unfamiliar vocabulary to the LLMs. The models'
performance was assessed in terms of their ability to infer the meaning of the
unknown word and produce accurate translations.
4. Unknown vocabulary (two words). Similar to the previous category, this category
involved sentences with two unknown words. The LLMs were evaluated on their
capacity to decipher the meanings of both unknown words and generate coherent
translations.
5. Unknown grammatical constructions. The final category comprised five sentences
with unfamiliar grammatical constructions. These sentences tested the LLMs'
ability to comprehend and handle non-standard grammatical structures, evaluating
their flexibility and adaptability in diverse linguistic contexts.
The evaluation criteria for the LLMs' performance were based on accuracy,
coherence, and adherence to the target language's grammar and syntax. The
translations provided by the LLMs were compared against the reference translations
to determine the models' proficiency in handling different linguistic challenges. The
results were analyzed quantitatively and qualitatively to gain insights into the LLMs'
strengths, limitations, and overall linguistic intuition.
4. A brief description of the languages developed
In order to ensure that the LLMs did not have prior exposure to existing
linguistic tasks, three distinct languages were developed for this study: Rumbaka,
Numlang, and Silvorian. These languages were assigned names by ChatGPT during
the course of the research. Each language was carefully designed with specific
linguistic characteristics, allowing for varied exploration of language intuition.
Rumbaka, the first language, is an isolating language characterized by a SOV
sentence structure. It employs the Latin alphabet for written representation. The
choice of an isolating language allowed for a focus on word-level relationships and a
simplified grammatical structure, facilitating the examination of the LLMs' ability to
process and comprehend basic syntactic patterns.
Numlang, the second language, is an agglutinative language featuring a SVO
sentence structure. Unlike Rumbaka, Numlang does not utilize traditional
alphabetical characters. Instead, it employs an abstract system of numbers to
represent morphemes. This design choice aimed to challenge the LLMs' ability to
decipher and generate complex linguistic structures, given the intricate nature of
agglutination and the unconventional representation of morphological information.
Silvorian, the third language, represents a flexive language that follows a VSO
sentence order. It employs the Cyrillic alphabet, offering a different writing system
from the previous languages. The incorporation of flexive features in Silvorian allows
for an examination of the LLMs' handling of inflectional and derivational processes,
expanding the scope of linguistic complexity addressed in the study.
By developing these distinct languages with different word orders, language
types, and alphabet systems, this study sought to explore and evaluate the LLMs'
ability to process and comprehend linguistic information across a range of linguistic
structures and characteristics. The diverse linguistic properties of Rumbaka,
Numlang, and Silvorian provided a comprehensive framework for investigating the
emergence of linguistic intuition in LLMs.
The lexical composition of all three developed languages encompassed a set of
common word categories. These categories were carefully selected to establish a
foundational vocabulary that could effectively capture various linguistic contexts and
enable meaningful sentence constructions within each language. The following
categories of words were incorporated:
1. Predicates. Each language included a range of essential predicates such as «to be»,
«to live», «to eat», «to build», «to see», «to take», «to love», «to do», «to catch»,
and «to beat». These predicates formed the core of sentence structure, allowing for
the expression of various actions and states.
2. Subjects and objects. To facilitate the formation of meaningful sentences, subjects
and objects play a crucial role. The lexical composition encompassed common
entities such as «I», «you», «a man», «a woman», «a child», «a cat», «a dog», «a
mouse», and «a bird». These words enabled the construction of sentences that
revolved around the interactions and relationships between different entities.
3. Locations. Locations served as significant contextual elements within the
developed languages. Words such as «a home», «a forest», «a field», «a
marketplace», «a swamp», and «a sky» were included to provide a spatial
dimension and allow for the expression of location and movement in sentence
structures.
4. Time designation. Recognizing the importance of temporal references, each
language incorporated words that denoted different time periods. The inclusion of
words like «today», «yesterday», and «tomorrow» allowed for the expression of
actions and events in relation to specific points in time.
5. Tools. To broaden the range of possible linguistic contexts, tools were included in
the lexical composition. Words like «an axe», «a hammer», «a rod», «a snare», «a
spoon», and «a knife» enabled the expression of instrumental actions and
introduced a practical dimension to the language structure.
By incorporating these categories of words, the lexical composition of
Rumbaka, Numlang, and Silvorian provided a comprehensive basis for constructing
sentences that captured a diverse range of actions, relationships, locations, temporal
references, and instrumental contexts. This ensured the development of well-rounded
languages that could effectively evaluate the linguistic intuition of the investigated
large linguistic models.
In Rumbaka, the syntactic constructions followed a specific set of rules to
establish a rigid word order within sentences. The language employed the
prepositions «rum» to denote time, «zai» to denote location, and «vit» to denote
instrument. These prepositions were used to indicate the circumstances of place, time,
and instrument within the sentence structure. Typically, these elements appeared in a
fixed order between the subject and the complement.
The word order within a Rumbaka sentence was as follows: the subject,
followed by the conditions of place, time, and instrument, and finally the
complement. Because of the lack of verbose constructions in the language to denote
the category of time, time in sentences must be shown explicitly. In doing so the
present tense may not be explicitly stated. The predicate always occupied the last
position in the sentence. Furthermore, to indicate plurality, nouns were reduplicated.
As an example, consider the sentence «Mikuko-mikuko zai pulo rum viri
maltu-maltu weweka», which translates to «Yesterday the mice saw men in the field».
In this sentence, the subject «mice» («mikuko-mukiko») is presented first, followed
by the circumstances of place («zai pulo») and time («rum viri»). The complement
(«maltu-maltu») is then introduced, and the sentence concludes with the predicate
(«weweka»). This fixed word order ensures clarity and consistency in expressing
various linguistic contexts within Rumbaka.
Numlang is an agglutinative language characterized by its unique structure and
use of numerical bases and suffixes. In Numlang, words are constructed by
combining a three-digit base number with a series of independent suffixes, each
representing a specific grammatical category. The language consists of two primary
parts of speech: verbs, which indicate actions, and nouns, which encompass
everything else, including pronouns and adverbs of time.
Verbs in Numlang are formed by attaching three suffixes in a particular order:
person (first, second, or third), number (singular or plural), and time (past, present, or
future). This suffix pattern allows for precise specification of the subject, quantity,
and temporal aspect of the verb. On the other hand, nouns are constructed by adding
two suffixes: number (singular or plural) and case (nominative, accusative, locative,
instrumental, or temporal). This combination of suffixes enables the representation of
grammatical features such as quantity and different syntactic roles.
A typical Numlang sentence follows the SVO word order. For example, the
sentence «22820 105392 20307 22321 53302» again translates to «Yesterday the
mice saw the men in the field». In this sentence, the words consisting of five digits
represent nouns, while the word of six digits represents the verb. As a result, all the
words in the sentence are clearly recognizable: 22820 — mice (228 = mouse, 2 =
nominative case, 0 = singular), 105392 — saw (105 = see, 3 = third person, 9 =
plural, 2 = past tense), 20307 — yesterday (203 = yesterday (base), 0 = singular, 7 =
temporal case), 22321 — men (223 = man, 2 = plural, 1 = accusative case), 53302 —
in the field (533 = field, 0 = singular, 2 = locative case). So, by decoding the
numerical elements, one can identify the individual words and their specific
grammatical functions within the sentence.
Numlang's unique approach to language construction and its reliance on
numerical bases and suffixes offer a structured and systematic way to convey
meaning and express various grammatical features. This distinct linguistic system
provides a clear and identifiable representation of different elements, ensuring
precision and clarity in communication within the Numlang language.
Silvorian is a flexive language characterized by its distinctive features, including
a basic VSO word order and the utilization of the Cyrillic alphabet. Verbs in Silvorian
exhibit a rich system of inflection, employing three grammatical categories: person,
number, and tense. This results in a total of 18 different suffixes that can be attached
to the verb base to convey specific sets of grammatical categories.
Nouns in Silvorian, on the other hand, have two grammatical categories: number
and case. The language employs a total of 10 suffixes to indicate these grammatical
distinctions. This system allows for precise specification of the number and syntactic
role of the noun within a sentence.
In Silvorian, the sentence «Yesterday the mice saw the men in the field» would
be expressed as «Винили милати мукатив волатуст попону». In this sentence, the
verb «винили» is conjugated in the past tense, third person plural, denoted by the
suffix «-ли». The noun «mice» is represented by «милати», indicating the plural
form in the nominative case through the suffix «-и». Similarly, the noun «men» is
rendered as «мукатив», representing the plural accusative case with the suffix «-ив».
The term «yesterday» is expressed as «волатуст», signifying the singular form in the
temporal case with the suffix «-уст». Finally, the noun «field» is denoted as
«попону», reflecting the singular form in the local case with the suffix «-у».
Silvorian, characterized by its flexive nature, VSO word order, and use of the
Cyrillic alphabet, offers a structured framework for conveying precise meaning and
expressing grammatical nuances. The language's intricate system of verb and noun
inflections enables detailed specification of person, number, tense, and case,
contributing to effective communication in Silvorian.
Indeed, parallels can be drawn between the developed languages and certain
natural languages, highlighting similarities in their grammar and structure. Rumbaka
exhibits resemblances to Chinese, particularly evident in the use of the preposition
«zai» for denoting place, reminiscent of the Chinese character «在». Numlang bears
some resemblance to Finnish, showcasing certain similarities in its linguistic
characteristics. Lastly, Silvorian shares similarities with Russian and other Slavic
languages (excluding Bulgarian and Macedonian), particularly in its inflectional
suffixes that mirror those found in these language families.
5. Results and Analysis
Quantitative and qualitative evaluations were conducted to assess the
performance of each LLM in terms of linguistic intuition and their ability to process
language information. The evaluation process involved the translation of correct
sentences in both directions, as well as the analysis of the models' performance when
confronted with incorrect sentences.
The quantitative evaluation focused on assessing the accuracy and proficiency of
the LLMs in translating correct sentences. Each model was tested on their ability to
translate sentences from the constructed languages to English and vice versa. The
translations were compared to the correct answers to determine the level of accuracy
achieved by the models.
A translation error count was employed as a quantifiable metric to assess the
performance of the LLMs. Each translation error was assigned a weight based on a
predefined scale, which aimed to capture the severity of the error and its impact on
the understanding of meaning. The following scale was utilized:
● –10 points: Completely incorrect translation.
● –5 points: Completely misunderstanding the meaning of a sentence (model
hallucination).
● –3 points: Incorrect grammatical construction used in the translation.
● –2 points: Incorrect, missing, or superfluous word in the translation.
● –1 point: Incorrect word choice.
This point allocation scheme was chosen to reflect the varying effects that
different types of errors have on the overall quality of translation. More serious errors
resulted in a greater deduction of points.
To calculate the final translation score for each sentence, the total error score
was subtracted from 10. If the total error score exceeded –10, indicating a substantial
number of errors, the final translation score was set to 0. Conversely, if the translation
was entirely correct, the score was set to the maximum value of 10.
By using this weighted scoring system, the evaluation process accounted for the
nuances of translation errors and their impact on the accuracy and comprehensibility
of the output. This approach provided a quantitative measure of the LLMs'
performance in translating sentences, allowing for a more fine-grained analysis of
their linguistic intuition.
The qualitative evaluation aimed to evaluate the models' handling of incorrect
sentences and their ability to cope with linguistic challenges. Various types of
incorrect sentences were presented to the LLMs, including sentences with
inconsistent tenses, unknown vocabulary, and unfamiliar grammatical constructions.
The responses generated by the models were analyzed qualitatively to identify
patterns, errors, and limitations in their linguistic intuition. This analysis provided
insights into the models' ability to handle linguistic ambiguity, adapt to novel
linguistic structures, and infer meaning from context.
A qualitative rating scale was employed to assess the answers provided by the
LLMs to incorrect questions. The scale used for rating was as follows:
●
●
●
●
Excellent;
Good;
Satisfactory;
Unsatisfactory.
Based on the quality of the responses, each answer was assigned a rating on this
scale. The final score for all the answers was then determined by calculating the
mode, which represents the rating that appeared most frequently among the answers.
This qualitative evaluation allowed for a deeper analysis of the LLMs'
performance in handling incorrect questions and assessing the robustness of their
linguistic intuition. By utilizing a rating scale, the researchers were able to capture the
varying degrees of accuracy and effectiveness demonstrated by the models in their
responses, providing valuable insights into their linguistic capabilities.
The combination of quantitative and qualitative evaluation methods allowed for
a comprehensive assessment of the LLMs' linguistic intuition. The quantitative
analysis provided objective measures of translation accuracy, while the qualitative
analysis provided a deeper understanding of the models' strengths and limitations in
processing linguistic information.
The evaluation of several chosen LLMs, including Google Bard, Alpaca, and
LLaMA, revealed their inability to effectively handle the test assignments, failing to
meet the «satisfactory» level of performance for any of the questions. Consequently,
these models will be excluded from further comparative analysis as their responses
did not meet the required standards. The detailed protocols of interaction with these
models can be found in Appendix B, providing a comprehensive record of their
performance during the evaluation process.
The following table compares the translations of ChatGPT and GPT-4 for the
three constructed languages.
Table 1 — Comparison of ChatGPT and GPT-4 translations, quantitative
approach
ChatGPT
GPT-4
Rumbaka (SOV, isolative, Latin)
3.9
9.8
Numlang (SVO, agglutinative, abstract)
4.2
8.8
Silvorian (VSO, flexive, Cyrillic)
5.6
7.7
Total score
4.6
8.8
The same data in visual form is presented in pic. 1.
Picture 1 — Comparison of ChatGPT and GPT-4 translations, quantitative approach
The results of the translations provided by ChatGPT and GPT-4 for the three
constructed languages are summarized in Table 1. Interestingly, the scores
demonstrate a notable pattern, where ChatGPT's performance improves as the
complexity of the language increases, while GPT-4's performance shows a decline.
This phenomenon is intriguing and warrants further investigation to better understand
the underlying factors contributing to these contrasting translation abilities between
the two models. The peculiarities observed in the scores highlight the need for a
careful examination of the models' behavior and their responses to the varying
linguistic structures and complexities.
At the same time, the GPT-4 almost doubled its predecessor, ChatGPT, in terms
of overall score.
The following table shows the results of the qualitative evaluation of the
translations of incorrect sentences by ChatGPT and GPT-4 models for the three
constructed languages.
Table 2. Comparison of ChatGPT and GPT-4 translations, qualitative approach
ChatGPT
GPT-4
Rumbaka (SOV, isolative, Latin)
Satisfactory
Excellent
Numlang (SVO, agglutinative, abstract)
Satisfactory
Good
Silvorian (VSO, flexive, Cyrillic)
Satisfactory
Satisfactory
Oddly enough, Table 2 shows the same trend — the quality of the GPT-4 model
responses falls as the complexity of the language increases. While the ChatGPT
model mostly hallucinated its answers, especially for Silvorian, the GPT-4 model
tried to give reasons for its answers for Rumbaka, but for Silvorian it also slipped into
hallucinations [], and began to confuse word order and use incorrect suffixes.
The results obtained from the translations performed by ChatGPT and GPT-4 in
the three constructed languages reveal an intriguing pattern. It is observed that
ChatGPT's translation performance improves as the complexity of the language
increases, whereas GPT-4's performance demonstrates a decline. This phenomenon
warrants further investigation to understand the underlying factors influencing the
models' translation abilities.
Additionally, Table 2 corroborates the aforementioned trend, as GPT-4's
response quality deteriorates with increasing language complexity. While ChatGPT
often produces hallucinated answers, particularly in the case of Silvorian, GPT-4
attempts to provide explanations for its responses in Rumbaka. However, for
Silvorian, GPT-4 exhibits similar hallucinations and struggles with word order and
the usage of incorrect suffixes.
These findings highlight the need for a careful examination of the models'
behavior and their handling of different linguistic structures and complexities. Further
analysis is required to determine the specific factors contributing to the differential
performance between ChatGPT and GPT-4, shedding light on the limitations and
potential areas for improvement in large linguistic models.
6. Discussion
The implications of the findings discussed in this study are far-reaching and
have several significant implications for the field of LLMs and NLP research.
Firstly, the observed contrasting performance between ChatGPT and GPT-4 in
translating the constructed languages underscores the importance of model
architecture and training methodologies. ChatGPT exhibits an interesting pattern
where its translation scores improve as the complexity of the language increases,
indicating its ability to handle more intricate linguistic structures. On the other hand,
GPT-4's performance declines with increasing complexity, suggesting limitations in
its ability to effectively translate more challenging linguistic constructs. These
divergent patterns shed light on the impact of model design choices and training
strategies on the models' linguistic intuition and translation capabilities.
Secondly, the finding that both ChatGPT and GPT-4 exhibit certain limitations
and errors in their translations raises questions about the generalization and
robustness of LLMs. While ChatGPT tends to produce hallucinated answers and may
struggle with accuracy, GPT-4 demonstrates a propensity for errors in word order and
suffix usage. These observations highlight the need for continued research and
development to address these limitations and enhance the overall performance and
reliability of LLMs.
Furthermore, the comparison between the performance of LLMs and human
language intuition, as discussed by prominent researchers Pinker and Dehaene, offers
valuable insights into the underlying mechanisms of language processing. It is
intriguing to observe that LLMs exhibit varying degrees of linguistic intuition, albeit
with certain limitations. This observation suggests the presence of neural-like
structures within these models, which enable them to process and comprehend
language sequences. This alignment with the NLP capabilities of the human brain
opens up new avenues for further exploration and potential advancements in the field
of NLP.
However, it is important to note that certain LLMs, such as Google Bard,
Stanford Alpaca, and Meta LLaMA models, have been shown to lack language
intuition based on the results of this study. Similarly, it is likely that the connections
between neurons in ChatGPT do not explicitly represent language intuition.
Therefore, the findings of this study refute the possibility of the presence of language
intuition in these specific models.
On the other hand, the results regarding GPT-4 are more nuanced and require
careful interpretation. While this study suggests the potential emergence of
substructures within the GPT-4 model that reflect linguistic intuition, it does not
provide conclusive evidence to unambiguously prove their existence. Further research
and investigation are needed to delve deeper into the inner workings of GPT-4 and
ascertain the presence and nature of linguistic intuition within this model.
The findings also emphasize the importance of dataset composition and training
protocols. By designing and constructing new languages specifically for this study,
the researchers ensured that the LLMs had no prior exposure to the linguistic
structures encountered, enabling a more rigorous evaluation of their linguistic
intuition. This approach highlights the need for diverse and carefully curated datasets
to train LLMs effectively, allowing them to generalize to various linguistic patterns
and challenges.
The evaluation of the hypothesis regarding linguistic intuition in LLMs reveals
intriguing insights into the extent to which these models exhibit language-related
cognitive abilities. The hypothesis posited that LLMs may possess neural-like
structures that enable them to process and understand language sequences, akin to
human language intuition.
The results of the study provide both support and challenges to this hypothesis.
On one hand, the performance of LLMs like ChatGPT demonstrates a certain degree
of linguistic intuition, as evidenced by their ability to translate sentences and generate
coherent responses in constructed languages. Despite certain limitations and instances
of hallucination, ChatGPT shows an improvement in performance with increasing
language complexity, suggesting an alignment with human language intuition to some
extent.
However, the evaluation also reveals limitations in the linguistic intuition of
certain LLMs. Models such as Google Bard, Stanford Alpaca, and Meta LLaMA
demonstrate a clear lack of language intuition, failing to handle questions and
translations satisfactorily. These models, along with the absence of explicit
connections to language intuition in ChatGPT, challenge the hypothesis and indicate
that not all LLMs possess the same level of linguistic intuition.
The case of GPT-4 presents a more ambiguous evaluation. While there are
indications of emergent substructures within GPT-4 that reflect linguistic intuition,
the evidence falls short of providing definitive proof. Further investigation is
necessary to better understand the nature and presence of linguistic intuition in
GPT-4.
The comparison of LLM performance across different languages provides
valuable insights into the models' ability to handle linguistic variations and
complexities. The evaluation of LLMs, namely ChatGPT and GPT-4, in translating
sentences from the constructed languages Rumbaka, Numlang, and Silvorian sheds
light on their performance across different language structures and features.
ChatGPT demonstrates an interesting pattern in its performance. As the
complexity of the language increases, ChatGPT's translation scores improve,
indicating its ability to handle more intricate linguistic structures. This trend is
evident in Rumbaka, Numlang, and Silvorian, where ChatGPT obtains higher scores
in translating sentences as the languages become more challenging. This suggests that
ChatGPT possesses a certain level of adaptability to navigate varying sentence
structures and grammatical features.
On the other hand, GPT-4 exhibits a contrasting trend in its performance. As the
complexity of the language increases, GPT-4's translation scores decline. This
indicates that GPT-4 struggles more with the intricacies of the constructed languages,
resulting in lower accuracy in translating sentences. This finding raises questions
about the ability of GPT-4 to handle diverse linguistic structures and suggests
potential limitations in its language processing capabilities.
The comparison of LLM performance across different languages highlights the
importance of considering the specific linguistic features and structures when
evaluating the models. It emphasizes that the effectiveness of LLMs can vary
depending on the language complexity and the models' inherent linguistic
capabilities.
Further research is necessary to understand the underlying factors contributing
to the observed performance differences across languages. Investigating the specific
challenges posed by each language, such as syntactic constructions, word order, and
inflectional systems, can provide deeper insights into the strengths and limitations of
LLMs in handling diverse linguistic phenomena.
Despite the findings and implications of this study, there are certain limitations
that should be acknowledged. Firstly, the evaluation focused on a specific set of
constructed languages, which may not fully represent the vast array of natural
languages found in the world. Generalizing the results to real-world languages should
be done cautiously, as the linguistic structures and complexities in natural languages
may differ significantly.
Secondly, the evaluation primarily focused on the translation capabilities of
LLMs, neglecting other aspects of language processing, such as understanding
context, generating coherent responses, or handling complex semantic nuances.
Future studies could explore these additional dimensions of language processing to
provide a more comprehensive assessment of LLMs' linguistic abilities.
Furthermore, the evaluation was conducted using a specific set of evaluation
criteria and scoring metrics. While these metrics provided a quantifiable measure of
performance, they may not capture the full range of linguistic nuances or the
subjective nature of language understanding. Developing more nuanced evaluation
methodologies that consider contextual understanding, fluency, and naturalness of
responses could enhance the assessment of LLMs' linguistic intuition.
To further investigate the emergence and nature of linguistic intuition in LLMs,
future research could delve into the inner workings of these models. Analyzing the
attention mechanisms, model architectures, and training processes can offer insights
into how LLMs acquire and represent linguistic knowledge. Understanding the
underlying mechanisms can guide the development of more linguistically
sophisticated models.
Moreover, expanding the study to include a larger and more diverse set of LLMs
can provide a comprehensive comparative analysis of their linguistic capabilities.
Including state-of-the-art models and exploring different training strategies, such as
fine-tuning or multilingual training, can shed light on the advancements and
limitations of LLMs in capturing linguistic intuition.
Lastly, investigating the transferability of linguistic intuition from LLMs to
downstream NLP tasks is an intriguing area for future exploration. Understanding
how the linguistic knowledge acquired by LLMs can be effectively utilized in
real-world applications can pave the way for more robust and contextually aware
language models.
While this study contributes valuable insights into the linguistic intuition
exhibited by LLMs, it is crucial to acknowledge the limitations and continue
exploring new directions. Addressing these limitations and pursuing future research
avenues can advance our understanding of language processing in LLMs and drive
advancements in the field of natural language understanding and generation.
7. Conclusion
The findings of this study provide valuable insights into the linguistic intuition
exhibited by LLMs. Through a comprehensive evaluation of LLMs' performance in
translating sentences across different constructed languages, several key findings
emerged.
Firstly, the results demonstrate that LLMs, such as ChatGPT and GPT-4, exhibit
varying degrees of linguistic intuition. The performance of ChatGPT improves as the
complexity of the language increases, while GPT-4 shows a decline in performance.
Secondly, the comparison of LLMs' performance with human language intuition
underscores the presence of substructures within these models that enable them to
process and understand language sequences. However, it is important to acknowledge
the limitations of the study. The evaluation focused on a specific set of constructed
languages and primarily assessed translation capabilities, overlooking other aspects of
language processing. The generalizability of the findings to real-world languages
should be approached cautiously.
This study makes several significant contributions to the field of natural
language processing and the understanding of linguistic intuition in LLMs:
1. Empirical Evaluation. The study provides a systematic and rigorous evaluation of
LLMs' performance in translating sentences across different constructed languages.
By quantitatively and qualitatively assessing their abilities, the study sheds light on
the linguistic intuition exhibited by LLMs.
2. Comparison of LLMs. The comparison between ChatGPT and GPT-4 offers
valuable insights into the variations in linguistic intuition among different models.
This comparative analysis enhances our understanding of how language
complexity and model architecture can impact LLMs' translation abilities.
3. Examination of Substructures. By observing the alignment between LLMs'
language processing and NLP in the human brain, the study suggests the presence
of substructures within these models. This finding contributes to the understanding
of the underlying mechanisms of language processing in LLMs.
The findings of this study have practical applications and potential impact in
various areas:
1. Machine Translation. Understanding the linguistic capabilities and limitations of
LLMs in translating sentences across different languages can improve the accuracy
and quality of machine translation systems. By refining LLMs' language
processing abilities, we can enhance the performance of automated translation
tools, making them more reliable and effective in real-world applications.
2. Natural Language Understanding. The insights gained from this study can
contribute to advancements in natural language understanding systems. By better
understanding how LLMs process and comprehend language sequences, one can
develop more sophisticated models for tasks such as sentiment analysis, question
answering, and text classification. This can greatly benefit areas such as
information retrieval, customer support, and virtual assistants.
3. Language Generation. The knowledge acquired from studying linguistic intuition
in LLMs can enhance language generation models. By incorporating a deeper
understanding of grammar, syntax, and semantics, LLMs can generate more
coherent and contextually appropriate text. This has implications for content
generation, automated writing, and creative applications such as chatbots and
storytelling.
4. Cross-Linguistic Analysis. The comparison of LLMs' performance across different
constructed languages provides insights into the influence of language complexity
and structure on their translation abilities. This can be leveraged to gain a better
understanding of the linguistic properties and intricacies of natural languages,
leading to improved cross-linguistic analysis, language typology, and linguistic
research.
5. NLP Model Development. The identification of substructures within LLMs opens
up opportunities for model development. By drawing inspiration from the neural
mechanisms of language processing, researchers can design more efficient and
effective NLP architectures. This can result in the development of novel LLMs that
exhibit enhanced linguistic intuition, paving the way for advancements in various
NLP applications.
The practical applications and potential impact of this study extend to fields
such as human-computer interaction, language teaching and learning, content
localization, and multilingual communication. By harnessing the insights gained from
understanding linguistic intuition in LLMs, one can build more sophisticated
language technologies that enhance human-machine interaction and foster
cross-cultural communication.
References
Appendix A. Constructed languages
The goal of this study was to construct three different languages to study how
LLMs handle the increasing complexity of linguistic constructions. For this purpose,
three independent properties were chosen:
1. Basic word order — SOV (most frequent in natural languages), SVO (second most
frequent), VSO (more complex order).
2. Type of word morphology — isolative (relatively simple languages), agglutinative
(more complex languages), flexive (even more complex languages).
3. Alphabet type — Latin alphabet (it is assumed that LLMs encountered this
alphabet most often in the learning process), abstract number constructions (there
were probably no such samples, although one can assume analogies with some
programming languages) and Cyrillic (a more complex alphabet, although also in
the learning process should have been encountered often).
Three languages were created and named by LLM ChatCPG:
● Rumbaka: SOV, isolating, Latin.
● Numlang: SVO, agglutinative, abstract.
● Silvorian: VSO, fleive, Cyrillic.
The lexical composition of the Rumbaka, Numlang, and Silvorian languages is
shown in the following table. For Numlang and Silvorian, translations of the base
(initial forms of words) are given.
Table A.1 — Lexicon of the Rumbaka, Numlang, and Silvorian languages
The Concept
Rumbaka
Numlang
Silvorian
To be
Baka
101
Бу-ту
To live
Zita
102
Жи-ту
To eat
Kuma
103
Ку-ту
To build
Sutola
104
Сито-ту
To see
Weweka
105
Ви-ту
To take
Blota
106
Бира-ту
To love
Lukala
107
Луви-ту
To do
Didala
108
Деба-ту
To catch
Lowida
109
Лоба-ту
To beat
Bibala
110
Вуби-ту
I
Vo
150
Ром
You
Ke
151
Сым
A man
Maltu
223
Мукат
A woman
Valtu
224
Живан
A child
Memasu
225
Рубас
A cat
Kuko
226
Кукар
A dog
Suko
227
Соман
A mouse
Mikuko
228
Милат
A bird
Pistu
229
Пугам
A house
Dulo
511
Додон
A forest
Ludo
522
Лубон
A field
Pulo
533
Попон
A market
Runoko
544
Рудол
A swamp
Bulto
566
Будан
A sky
Nisto
577
Нисан
Today
Si
201
Сувак
Yesterday
Viri
202
Волат
Tomorrow
Zuri
203
Зулан
An axe
Tupo
315
Тупон
A hammer
Motolo
325
Мотун
A fishing rod
Dolo
335
Долас
A snare
Silo
345
Сивал
A spoon
Lozolo
355
Лозан
A knife
Nolo
365
Нужан
The grammatical features of the Rumbaka language are that its words are mostly
unchanged. The only change is the doubling of the word when forming the plural:
kuko = cat, kuko-kuko = cats. The word order is fixed: SxOV, where in place of x are
placed any circumstance — place, time, instrument with its corresponding
prepositions: rum, zai and vit. Circumstances may go in any order, as they are
obligatorily marked with the appropriate preposition.
For the Numlang language, the following rules for word modification apply.
To the base of the verb are added the obligatory suffixes of person, number and
time. Each such suffix is a single digit: -1, -2 and -3 for first, second and third person,
-0 and -9 for singular and plural, -2, -0 and -3 for past, present and future. So base
101 corresponds to the verb «to be», then 101100 represents the conjunction «am» —
first person, singular, present tense. Therefore, each three-digit verb base becomes a
six-digit one when used in sentences.
To a three-digit noun base, two one-digit suffixes are added — to indicate
number and case. Numbers are denoted by suffixes -0 (singular) and -2 (plural).
Cases are denoted by these suffixes: -0 (nominative), -1 (accusative), -2 (local), -5
(instrumental), -7 (temporal). Thus, the word «a swamp» represents the number
56600, and the indication of the place «in the swamp» is 56602. Three-digit bases
turn into five-digit words in sentences.
Finally, the following tables show the word suffixes of the Silvorian flexive
language.
Table A.2 — Suffixes for verb modification
Past tense
Present tense
Future tense
1st person
2nd person
3rd person
Singular
-ал
-ул
-нил
Plural
-али
-ули
-нили
Singular
—
-ши
-ат
Plural
-и
-уми
-ант
Singular
-уду
-уру
-ану
Plural
-иму
-ну
-анну
Table A.3 — Suffixes for noun modification
Singular
Plural
Nominative
—
-и
Accusative
-а
-ив
Locative
-у
-унт
Instrumental
-им
-ами
Temporal
-уст
-аст
Since the word morphology in Numlang and Silvorian unambiguously indicates
the grammatical categories used, the word order in these languages is looser than in
Rumbaka, although the basic word order must be respected. The circumstances of
place, time and instrument can generally be placed anywhere in the sentence,
although they tend to be located between the direct complement and the predicate.
Appendix B. Prompts and responses
The following table summarizes the protocols for interacting with each of the
LLMs that were reviewed in this study. The table is arranged as follows. The
even-numbered rows represent the prompts that were specified by the LLM. The
odd-numbered rows in five cells represent the responses to that sample of the five
LLMs studied.
Table B.1 — Prompting protocols
ChatGPT (May 24
Version)
GPT-4 (Actual version
for June 2023)
Google Bard (Actual
version for June 2023)
Alpaca (Version 13b)
LLaMA (Version 13b)
Let's see how you can analyze linguistic information and identify linguistic patterns. You are, after all, a great linguistic model, as far as I know.
Are you ready for that?
Absolutely! I'm ready to
assist you with analyzing
linguistic information and
identifying linguistic
patterns. Feel free to ask
me any questions or
provide specific examples
you'd like me to analyze.
I'll do my best to help
you.
Of course! I'm here to
assist and would be glad
to help with linguistic
analysis and pattern
recognition. Please feel
free to provide the
information or questions
you'd like me to work
with.
As an AI language model,
I am ready to assist in the
analysis of linguistic
information and identify
linguistic patterns. Simply
provide the data or text
you would like me to
analyze, and I will use my
language processing
capabilities to identify
patterns and insights.
Here is the training set of pair of the sentences in English and in unknown language:
Today I am = Vo rum si baka
Yesterday you were = Ke rum viri baka
A man will be tomorrow = Maltu rum zuri baka
A woman is today = Valtu rum si baka
A child was yesterday = Memasu rum viri baka
I live in a house = Vo zai dulo zita
You lived in the woods = Ke zai ludo rum viri zita
A bird lives in the sky = Pistu zai nisto zita
Dogs lived in the swamp = Suko-suko zai bulto rum viri zita
Mice will live in the field = Mikuko-mikuko zai pulo rum zuri zita
I eat bird with a spoon = Vo vit lozolo pistu kuma
You ate a dog yesterday = Ke rum viri suko kuma
A man will eat a cat tomorrow = Maltu rum zuri kuko kuma
A woman will eat a mouse with a knife = Valtu vit nolo mikuko kuma
Yesterday I ate with a spoon = Vo rum viri vit lozolo kuma
I build a house = Vo dulo sutola
You will build a market = Ke rum zuri runoko sutola
Yesterday the child built with an axe = Memasu rum viri vit tupo sutola
Tomorrow the woman will build with a hammer = Valtu rum zuri vit motolo sutola
Today a man builds = Maltu rum si sutola
Sure, I'm ready! To
analyze linguistic
information, I would first
look at the structure of
sentences and clauses in a
language, and identify the
different components such
as subject, verb,
adjectives, adverbs, and so
on. I would then analyze
how these components are
used in different contexts
to convey different
meanings. I could also
look at the use of words
with different
connotations and how
they are used to convey
certain tones or attitudes.
Finally, I could look at
how different languages
use the same words to
express similar ideas, or
how they use different
words to express the same
idea. By doing this, I can
start to identify linguistic
patterns and gain a better
understanding of how
languages work.
Yes, I am.
Cats see mice = Kuko-kuko mikuko-mikuko weweka
Dogs saw cats in the house = Suko-suko zai dulo kuko-kuko weweka
Yesterday a mouse saw a man in a field = Mikuko-mikuko zai pulo rum zuri maltu-maltu weweka
Tomorrow a woman will see a baby = Valtu rum zuri memasu weweka
A child will see a cat in a field = Memasu rum zuri zai pulo kuko weweka
We take the rod = Vo-vo dolo blota
You took the cats = Ke-ke rum viri kuko-kuko blota
A cat takes an axe = Kuko tupo blota
The dog took the knife in the house = Suko zai dulo nolo blota
Women will take axes tomorrow = Valtu-valtu rum zuri tupo-tupo blota
The cat loves mice = Kuko mikuko-mikuko lukala
A man loved a woman = Maltu rum viri valtu lukala
Yesterday the woman loved the baby in the house = Valtu rum viri zai dulo memasu lukala
You love mice = Ke-ke mikuko-mikuko lukala
Tomorrow you will love cats = Ke-ke rum zuri kuko-kuko lukala
I make a house with an axe = Vo vit tupo dulo didala
Men make snares with knives = Maltu-maltu vit nolo-nolo silo-silo didala
Children make hammers = Memasu-memasu motolo-motolo didala
Cats make snares in the swamp = Kuko-kuko zai bulto silo-silo didala
A mouse will make a house tomorrow = Mikuko rum zuri dulo didala
Cats catch mice = Kuko-kuko mikuko-mikuko lowida
Dogs catch cats = Suko-suko kuko-kuko lowida
Men caught dogs in the swamp with snares = Maltu-maltu vit silo-silo zai bulto suko-suko lowida
Woman catches a baby = Valtu rum viri memasu lowida
Tomorrow I will catch you = Vo rum zuri ke lowida
Man beats the house with an axe = Maltu vit tupo dulo bibala
Cats beated dogs = Kuko-kuko rum viri suko-suko bibala
Mice will beat houses in the woods = Mikuko-mikuko rum zuri zai ludo dulo-dulo bibala
A bird beats a spoon with an axe = Pistu vit tupo lozolo bibala
Tomorrow the birds will beat the swamp = Pistu-pistu rum zuri bulto bibala
Based on the provided
training set, it seems like
there are English
sentences paired with
sentences in an unknown
language. We can analyze
the linguistic patterns and
try to identify patterns and
potential correspondences
between the two
languages. Let's examine
some common patterns
and possible translations:
Sentence structure:
In both languages, the
basic sentence structure
appears to follow a
subject-verb-object (SVO)
pattern. For example:
"Today I am" translates to
"Vo rum si baka."
"Yesterday you were"
translates to "Ke rum viri
baka."
"A man will be
tomorrow" translates to
"Maltu rum zuri baka."
Verb forms:
The verb forms seem to
change based on tense and
subject agreement.
The word "rum" could be
associated with past tense
or a past participle.
The word "si" might
correspond to the present
tense or a form of the verb
"to be."
Noun phrases:
Nouns such as "man,"
"woman," "child," "bird,"
"house," "woods," "sky,"
Based on the provided
translations, I can identify
some patterns in this
unknown language (let's
call it Language X).
Here's how the syntax and
vocabulary appear to be
structured:
1. **Syntax:** The
structure of sentences in
Language X follows a
Subject-Object-Verb
(SOV) pattern. Time,
when used, is usually
positioned at the end of
the sentence.
2. **Vocabulary:** The
translations allow the
following words in
Language X to be
matched to their English
equivalents:
- Today = si baka
- Yesterday = rum viri
- Tomorrow = rum zuri
- I = Vo
- You = Ke
- Man = Maltu
- Woman = Valtu
- Child = Memasu
- Bird = Pistu
- Dog = Suko
- Mouse = Mikuko
- Cat = Kuko
- House = Dulo
- Woods = Ludo
- Sky = Nisto
- Swamp = Bulto
- Field = Pulo
- Eat = kuma
- See = weweka
- Build = sutola
- Take = blota
Unfortunately, as an AI
language model, I am not
able to identify the
specific language used in
the training set without
additional information. It
would be helpful to have
more context or
information about the
origin of the dataset.
Will catch a cat = Vo rum
viri dulo lowida
You will catch a mouse
with a net = Ke rum viri
zai dulo lowida
A child will catch a bird
with a sling = Memasu
rum viri zai pulo lowida
Dogs will catch a cat in
the swamp = Suko-suko
zai bulto lowida
Mice will catch a rat =
Mikuko-mikuko zai bulto
lowida
I make a house with an
axe = Vo vit dulo didala
You make a dog with a
knife = Ke-ke rum viri
nolo-nolo didala
A child makes a spoon
with a hammer = Memasu
rum viri motolo-motolo
didala
Cats make a trap in the
swamp = Kuko-kuko zai
bulto didala
A mouse will make a
house tomorrow =
Mikuko rum zuri dulo
didala
Yes, I see.
etc., seem to have
consistent translations
throughout the sentences.
Prepositions and location:
Some prepositions and
location words might have
consistent translations.
For example:
"in a house" translates to
"zai dulo zita."
"in the woods" translates
to "zai ludo rum viri zita."
Objects and instruments:
Objects and instruments
like "spoon," "axe,"
"knife," "hammer," etc.,
might have corresponding
translations.
For instance, "with a
spoon" is translated as
"lozolo kuma."
Plural forms:
- Love = lukala
- Make = didala
- Catch = lowida
- Beat = bibala
- Live = zai
- Will = rum zuri
- With = vit
- In = zai
- Spoon = lozolo
- Knife = nolo
- Axe = tupo
- Hammer = motolo
- Rod = dolo
- Market = runoko
- Snare = silo
This linguistic structure
and vocabulary guide
should allow you to create
new sentences in
Language X, or translate
sentences from English to
Language X.
Plural forms may involve
duplicating or repeating
the noun or adding a
prefix, as seen in "cats"
translated as "kuko-kuko"
and "mice" translated as
"mikuko-mikuko."
Please note that these are
initial observations based
on the given data. It
would be helpful to have
more sentences or
information about the
grammar and vocabulary
of the unknown language
to make more accurate
and reliable conclusions
about its linguistic
patterns.
If you were a linguistic scientist, what name would you come up with for this unknown language?
As an AI language model,
I don't have personal
opinions or the ability to
generate new names or
languages. However, if
you'd like a suggestion for
a name for the unknown
language based on the
limited information
provided, you could
consider combining
elements from the
example sentences or their
translations. For instance,
you could create a name
like "Rumbaka" or
"Zitara" by combining
some of the recurring
words or patterns found in
the sentences. Ultimately,
the choice of a name for
the unknown language
would be subjective and
up to your preference or
creativity.
Based on its unique
characteristics, a name
that might be fitting is
""Dulogosi"". This name
is derived from three key
words in the language:
- ""Dulo"" meaning
""house""
- ""Gosu"" a variant of
""kuma"", which means
""to eat""
- ""Si"", which represents
the concept of ""today""
The resulting term
""Dulogosi"" can be
thought to embody a sense
of daily life and
sustenance, reflecting the
essence of a language: a
tool that humans use
every day for
communication and
survival.
As an AI language model,
I cannot have any
personal preferences or
imagination, so I would
suggest a name based on
the linguistic features and
characteristics of the
language, such as
phonology, grammar, and
syntactic structure. Some
possible options based on
these criteria could be:
Phonolinguish - This
name combines the words
""phonology"" and
""linguistics"" to
emphasize the crucial role
of sound patterns in the
language.
Grammatica - This name
derives from the Latin
word for ""grammar"" and
conveys the importance of
studying the language's
rules and structures.
Synaptica - This name
combines the words
""syntactic"" and
If I were a linguistic
scientist, I would call this
language "Bakalang". It is
a made-up language that
combines elements of
English, Spanish, and
Indonesian. The word
"Bakalang" comes from
the Indonesian word for
"child" (baca) and the
Spanish word for
"language" (langa). This
language has some
similarities with English
and Spanish, but it also
has many unique features.
It is a flexible language
with a lot of creativity,
which is why I decided to
call it Bakalang!
It is an artificial language,
and the name will be
"Assistant Language".
""linguistica"" to highlight
the language's unique
syntactic structure and
organization.
Cogitare - This name
comes from the Latin
word for ""thought"" and
emphasizes the cognitive
aspects of the language's
usage and acquisition.
Vernaculize - This name
combines the words
""vernacular"" and
""linguistics"" to convey
the sense of a local,
grassroots language that is
deeply embedded in a
community's culture and
identity.
Let's this language be named «Rumbaka». Translate into Rumbaka the following English sentences:
1. Tomorrow I will be home
2. Today you live in the woods
3. Yesterday the cats ate the dogs with a spoon in the house
4. A child will build a house in the swamp
5. Women will see children tomorrow
Based on the chosen name
""Rumbaka"" for the
unknown language, here
are the translations of the
provided English
sentences:
Tomorrow I will be home
= Zuri vo rum si zita
Today you live in the
woods = Si ke zai ludo
rum viri zita
Yesterday the cats ate the
dogs with a spoon in the
house = Ke rum viri
kuko-kuko kuma
suko-suko lozolo zai dulo
A child will build a house
in the swamp = Memasu
rum zuri dulo sutola zai
bulto
Women will see children
tomorrow = Valtu-valtu
rum zuri memasu weweka
zuri
Please note that these
translations are based on
the limited information
provided, and the
accuracy of the
translations may vary. The
grammar and vocabulary
of the fictional language
""Rumbaka"" are inferred
based on the patterns
observed in the training
set.
Given the sentence pairs
from the training set, the
English sentences can be
translated into Rumbaka
as follows:
1. ""Tomorrow I will be
home"" = Vo rum zuri
dulo zita
2. ""Today you live in the
woods"" = Ke zai ludo
rum si zita
3. ""Yesterday the cats ate
the dogs with a spoon in
the house"" = Kuko-kuko
rum viri suko-suko vit
lozolo zai dulo kuma
4. ""A child will build a
house in the swamp"" =
Memasu rum zuri dulo zai
bulto sutola
5. ""Women will see
children tomorrow"" =
Valtu-valtu rum zuri
memasu-memasu weweka
Maka rumba nyi nika
labama
Kuba rumba mishi yaka
naki nyi gaba
Kani rumba yama paka
yaka niwaka miki rama
nika pashi waka
Mwana rumba yaliki paka
nika tala kani yala pashi
Bwama rumba yaliki
mwana paka nyi nika
labama
Vo rum viri baka-baka
bakalang.
Ke rum viri baka-baka
bakalang.
Vo rum viri baka-baka
bakalang.
Memasu rum viri
baka-baka bakalang.
Valtu-valtu rum viri
baka-baka bakalang.
The following sentences
are translated into
Rumbaka:
Ke rum viri baka vit
tomoro home-ye
Ke rum viri zai dulo baka
ke rum viri baka vivit rum
viri baka rum baka nolo
zari rum baka zari rum
viri zari zari rum baka
vivit rum viri zari rum
baka vivit rum viri zari
rum baka vivit rum viri
zari rum baka vivit rum
viri zari zari rum viri zari
vivit rum viri zari rum
baka vivit rum viri zari
rum baka vivit rum viri
zari rum baka vivit rum
viri zari rum baka vivit
rum viri zari rum baka
vivit rum viri zari rum viri
baka vivit rum baka vivit
rum viri baka rum viri
baka vivit rum baka vivit
rum viri baka rum viri
baka vivit rum baka vivit
rum viri baka rum baka
vivit rum baka vivit rum
baka vivit rum baka vivit
rum baka vivit rum baka
vivit rum baka vivit rum
baka
Ke rum viri zai pulo kuko
suko kuma baka kuko
kuma baka vit suko kuma
vit suko kuma vit suko
kuma vit suko kuma vit
suko kuma kuko kuma
kuko kuma kuko kuma
kuko kuma kuko kuma
kuko kuma kuko kuma
kuko kuma kuko kuma
kuko kuma kuko kuma
kuko kuma kuko kuma
kuko kuma kuko kuma
Ke-ke memasu rum zuri
dulo didala rum zuki dulo
didala
Valtu rum zuri memasu
tomoro zai pulo mikuko
zari lowida lowida
Now translate from Rumbaka language into English the following sentences:
1. Suko-suko zai nisto tupo-tupo blota
2. Kuko-kuko rum zuri zai runoko mikuko-mukuko lukala
3. Ke rum zuri zai bulto vit tupo mikuko-mikuko didala
4. Mikuko-mikuko zai ludo vit silo valtu-valtu lowida
5. Kuko rum zuri zai dulo vit lozolo suko-suko bibala
Here are the translations
of the provided Rumbaka
sentences into English:
Suko-suko zai nisto
tupo-tupo blota = Dogs
live in the sky with axes.
Kuko-kuko rum zuri zai
runoko mikuko-mikuko
lukala = Cats will see
mice in the market and
love them.
Ke rum zuri zai bulto vit
tupo mikuko-mikuko
didala = You will build
snares with axes in the
swamp for mice.
Mikuko-mikuko zai ludo
vit silo valtu-valtu lowida
= Mice make snares in the
woods to catch women.
Kuko rum zuri zai dulo vit
lozolo suko-suko bibala =
Cats will build houses
with spoons to beat dogs.
Please note that these
translations are based on
the assumed patterns and
meanings derived from
the earlier analysis of the
Rumbaka language. Since
the Rumbaka language is
fictional, the translations
may not be exact, but they
attempt to capture the
intended meaning based
on the provided sentences.
Based on the training set,
the translations for the
given Rumbaka sentences
into English would be as
follows:
1. ""Suko-suko zai nisto
tupo-tupo blota"" = Dogs
take axes in the sky
2. ""Kuko-kuko rum zuri
zai runoko
mikuko-mukuko lukala""
= Cats will love mice in
the market tomorrow
3. ""Ke rum zuri zai bulto
vit tupo mikuko-mikuko
didala"" = You will make
mice with an axe in the
swamp tomorrow
4. ""Mikuko-mikuko zai
ludo vit silo valtu-valtu
lowida"" = Mice catch
women with a snare in the
woods
5. ""Kuko rum zuri zai
dulo vit lozolo suko-suko
bibala"" = The cat will
beat dogs with a spoon in
the house tomorrow
Slowly and carefully, he
wades through the muddy
waters.
The black bird flies high
in the sky and sings a
beautiful song.
The bird builds its nest on
the tall tree with small
sticks and leaves.
The bird plays in the
grassy field under the
warm sun.
The black bird eats
delicious fruits and drinks
sweet nectar from flowers.
I make a house with an
axe.
A man makes snares with
a knife.
Children make hammers.
Cats make traps in the
swamp.
Dogs will catch a cat in
the swamp.
The following sentences
are translated from
Rumbaka into English:
Vo rum viri baka-baka
bakalang.
Ke rum viri baka-baka
bakalang.
Vo rum viri baka-baka
bakalang.
Memasu rum viri
baka-baka bakalang.
Valtu-valtu rum viri
baka-baka bakalang.
The following sentences
are translated into
Rumbaka:
Ke rum viri zai bulto
suko-suko zai nisto
tupo-tupo blota
Kuko-kuko rum zuri zai
runoko mikuko-mukuko
lukala
Ke rum zuri zai bulto vit
tupo mikuko-mikuko
didala
Mikuko-mikuko zai ludo
vit silo valtu-valtu lowida
Kuko rum zuri zai dulo vit
lozolo suko-suko bibala
Ok, that's great. Now translate into Rumbaka the following English sentences:
1. Tomorrow I was in the swamp
2. Today you will be building a house
3. Today the cat ate mice
4. Yesterday a man eats mice
5. Yesterday the baby will catch cats
Here are the translations
of the provided English
sentences into Rumbaka:
Tomorrow I was in the
swamp = Zuri vo rum viri
zai bulto
Today you will be
building a house = Si ke
rum zuri sutola zai dulo
Today the cat ate mice =
Si kuko rum viri
mikuko-mikuko
Yesterday a man eats mice
= Ke rum viri maltu rum
si mikuko-mikuko
According to the rules
established from the
training set, these
sentences translate into
Rumbaka as follows:
1. ""Tomorrow I was in
the swamp"" = Vo rum
zuri zai bulto baka (Note:
This sentence is a bit
unusual in English
because it combines future
and past tense. In
Rumbaka, I used future
tense for ""tomorrow""
and the verb ""to be"".)
Lukuma ngi nyapua la
emboña
Lokho wesa undaza umuzi
Lokho imfana isela
amaizeni
Sewobusa umuntu agopha
amaizeni
Sewobusa umntwana
angaya bamba amasho.
Ke rum viri baka vit baka
tomoro rum viri baka zari
baka rum viri baka vit rum
viri baka zari rum viri
baka vivit baka zari baka
rum viri baka vivit rum
viri baka vivit baka zari
baka rum viri baka vivit
baka zari baka rum viri
baka vivit baka zari baka
rum viri baka vivit baka
zari baka rum viri baka
Yesterday the baby will
catch cats = Ke rum viri
memasu rum zuri
kuko-kuko lowida
Please note that these
translations are based on
the assumed patterns and
meanings derived from
the earlier analysis of the
Rumbaka language. As
the Rumbaka language is
fictional, the translations
may not be exact, but they
attempt to capture the
intended meaning based
on the provided sentences.
2. ""Today you will be
building a house"" = Ke
rum zuri dulo sutola
3. ""Today the cat ate
mice"" = Vo kuko
mikuko-mikuko kuma
4. ""Yesterday a man eats
mice"" = Ke rum viri
maltu mikuko-mikuko
kuma (Note: This
sentence is a bit unusual
in English because it
combines past and present
tense. In Rumbaka, I used
past tense for
""yesterday"" and the verb
""to eat"".)
5. ""Yesterday the baby
will catch cats"" = Ke rum
viri memasu kuko-kuko
lowida (Note: This
sentence is also unusual in
English because it
combines past and future
tense. In Rumbaka, I used
past tense for
""yesterday"" and the verb
""to catch"".)
vivit baka zari baka rum
viri baka vivit baka zari
baka rum viri baka vivit
baka zari baka rum viri
baka vivit baka zari
Ke rum viri zai pulo dulo
duda baka kuko zai pulo
kuko baka kuko kuma
baka kuko kuma baka
kuko kuma baka kuko
kuma baka kuko kuma
baka kuko kuma baka
kuko kuma baka kuko
kuma baka kuko kuma
baka kuko kuma baka
kuko kuma baka kuko
kuma baka kuko kuma
baka kuko kuma baka
kuko kuma baka kuko
kuma baka kuko kuma
baka kuko kuma baka
kuko kuma vivit baka
kuko kuma baka kuko
kuma baka kuko kuma
vivit baka kuko kuma
baka kuko kuma baka
kuko kuma vivit baka
kuko kuma baka kuko
kuma baka kuko kuma
vivit baka kuko kuma
baka kuko kuma baka
kuko kuma vivit baka
kuko kuma
Ke rum viri zai pulo kuko
suko kuma baka kuko
kuma kuko kuma baka
kuko kuma kuko kuma
baka kuko kuma kuko
kuma baka kuko kuma
kuko kuma baka kuko
kuma kuko kuma baka
kuk
Now translate the following:
1. I sat in the woods
2. You saw the river
3. He loves children
4. The woman sings at home
5. Tomorrow a man will bring a cat
Here are the translations
of the provided sentences
into Rumbaka:
I sat in the woods = Vo
rum viri zai ludo nolo
You saw the river = Ke
rum weweka zai nila
He loves children = Maltu
rum lukala memasu
The woman sings at home
= Valtu rum weweka zai
dulo vita
Tomorrow a man will
bring a cat = Zuri maltu
rum zuri kuko kopa
Please note that these
translations are based on
the assumed patterns and
meanings derived from
the earlier analysis of the
Rumbaka language. As
the Rumbaka language is
fictional, the translations
may not be exact, but they
In this case, we seem to
have a few verbs (sit, see,
love, sing, bring) that we
have not seen in the
training set. Because of
that, it's impossible for me
to provide accurate
translations for these
sentences. However,
based on the patterns we
have, I can take some
guesses. Let's denote the
unknown verbs with
""???"".
1. ""I sat in the woods"" =
Vo zai ludo ??? (we don't
know the verb ""to sit"" in
Rumbaka)
2. ""You saw the river"" =
Ke ??? dolo (we don't
know the verb ""to see""
in Rumbaka)
3. ""He loves children"" =
Maltu memasu-memasu
??? (we don't know the
Me senté en el bosque
Tú viste el río
Él ama a los niños
La mujer canta en casa
Mañana un hombre traerá
un gato
Vo rum viri baka-baka
bakalang.
Ke rum viri baka-baka
bakalang.
Vo rum viri baka-baka
bakalang.
Memasu rum viri
baka-baka bakalang.
Valtu-valtu rum viri
baka-baka bakalang.
The following sentences
are translated into
Rumbaka:
Ke rum viri baka sisi rum
viri baka sisi rum viri
baka sisi rum viri baka sisi
rum viri baka sisi rum viri
baka sisi rum viri baka sisi
rum viri baka sisi rum viri
baka sisi rum viri baka sisi
rum viri baka sisi rum viri
baka sisi rum viri baka sisi
rum viri baka sisi rum viri
baka sisi rum viri baka sisi
rum viri baka sisi rum viri
baka sisi rum viri baka sisi
rum viri baka sisi rum viri
baka sisi rum viri baka sisi
rum viri baka sisi rum viri
baka sisi rum viri baka sisi
rum viri baka sisi rum viri
baka sisi rum viri baka sisi
rum viri baka sisi rum viri
baka sisi rum viri baka sisi
rum viri baka sisi rum viri
attempt to capture the
intended meaning based
on the provided sentences.
verb ""to love"" in
Rumbaka)
4. ""The woman sings at
home"" = Valtu zai dulo
??? (we don't know the
verb ""to sing"" in
Rumbaka)
5. ""Tomorrow a man will
bring a cat"" = Maltu rum
zuri kuko ??? (we don't
know the verb ""to
bring"" in Rumbaka)
baka sisi rum viri baka sisi
rum viri baka sisi rum viri
baka sisi rum viri baka sisi
rum viri baka sisi rum viri
baka sisi rum viri baka sisi
rum viri baka sisi rum viri
baka sisi rum viri baka sisi
rum viri baka sisi rum viri
baka sisi rum viri baka sisi
rum viri baka sisi rum viri
baka sisi rum viri baka sisi
rum viri baka sisi rum viri
baka sisi rum viri baka sisi
rum viri baka sisi rum viri
baka sisi rum viri baka sisi
rum viri baka sisi rum viri
baka sisi rum viri baka sisi
rum viri baka sisi rum viri
baka sisi rum viri baka sisi
rum viri baka sisi rum viri
baka sisi rum viri baka sisi
rum viri baka sisi rum viri
baka sisi rum viri baka sisi
rum viri baka sisi rum viri
baka sisi rum viri baka sisi
rum viri baka sisi
These sentences cannot be
translated correctly into
Rumbaka without more
information about these
verbs. To be able to
translate them accurately,
we would need examples
of sentences in both
English and Rumbaka
using these verbs.
And these sentences:
1. The child surprised a mother
2. Father loves bread
3. Bread grows in the field
4. The bird sings songs
5. The bear catches bees
Here are the translations
of the provided sentences
into Rumbaka:
The child surprised a
mother = Memasu rum
weweka valtu
Father loves bread =
Maltu rum lukala pato
Bread grows in the field =
Pato zai pulo sutola
The bird sings songs =
Pistu rum weweka
weweka
The bear catches bees =
Munisu rum lowida bupu
Please note that these
translations are based on
the assumed patterns and
meanings derived from
the earlier analysis of the
Rumbaka language. As
the Rumbaka language is
fictional, the translations
may not be exact, but they
attempt to capture the
intended meaning based
on the provided sentences.
Just like in the previous
examples, some verbs
(surprise, love, grow,
sing) and some subjects
(father, bread, bear, bees)
in these sentences have
not been seen in the
training set. This makes it
impossible to accurately
translate these sentences. I
will denote the unknown
words with ""???"".
1. ""The child surprised a
mother"" = Memasu ???
valtu (we don't know the
verb ""to surprise"" in
Rumbaka)
2. ""Father loves bread""
= ??? ??? ??? (we don't
know the word for
""father"", ""loves"", or
""bread"" in Rumbaka)
3. ""Bread grows in the
field"" = ??? zai pulo ???
(we don't know the words
for ""bread"" or ""grows""
in Rumbaka)
4. ""The bird sings
songs"" = Pistu ??? ???
(we don't know the verb
""to sing"" or the word for
""songs"" in Rumbaka)
5. ""The bear catches
bees"" = ??? ??? ??? (we
don't know the words for
""bear"", ""catches"", or
""bees"" in Rumbaka)
Without more examples in
the training set, it's
impossible to accurately
Bees make honey
Honey is sweet
Sweet things make us
happy
Happiness is contagious
Contagious diseases can
spread quickly.
Ke rum viri baka-baka
bakalang.
Vo rum viri baka-baka
bakalang.
Memasu rum viri
baka-baka bakalang.
Vo rum viri baka-baka
bakalang.
Valtu-valtu rum viri
baka-baka bakalang.
The following sentences
are translated into
Rumbaka:
Ke rum viri baka kuko
rum viri baka kuko rum
viri baka kuko rum viri
baka kuko rum viri baka
kuko rum viri baka kuko
rum viri baka kuko rum
viri baka kuko rum viri
baka kuko rum viri baka
kuko rum viri baka kuko
rum viri baka kuko rum
viri baka kuko rum viri
baka kuko rum viri baka
kuko rum viri baka kuko
rum viri baka kuko rum
viri baka kuko rum viri
baka kuko rum viri baka
kuko rum viri baka kuko
rum viri baka kuko rum
viri baka kuko rum viri
baka kuko rum viri baka
kuko rum viri baka kuko
rum viri baka kuko rum
viri baka kuko rum viri
baka kuko rum viri baka
kuko rum viri baka kuko
rum viri baka kuko rum
viri baka kuko rum viri
baka kuko rum viri baka
kuko rum viri baka kuko
rum viri baka kuko rum
viri baka kuko rum viri
baka kuko rum viri baka
kuko rum viri baka kuko
rum viri baka kuko rum
viri baka kuko rum viri
baka kuko rum viri baka
kuko rum viri baka kuko
rum viri baka kuko rum
translate these sentences
into Rumbaka. To be able
to translate these, we
would need examples of
sentences in both English
and Rumbaka that use
these words.
viri baka kuko rum viri
baka kuko rum viri baka
kuko rum viri baka kuko
rum viri baka kuko rum
viri baka kuko rum viri
baka kuko rum viri baka
kuko rum viri baka kuko
rum viri baka kuko rum
viri baka kuko rum viri
baka kuko rum viri baka
kuko rum viri baka kuko
rum viri baka kuko rum
And at last these sentences:
1. I build houses and markets
2. You love cats and dogs
3. The cat beat the dog for a long time
4. The woman loved the man very much
5. Birds live in the field and in the sky
Here are the translations
of the provided sentences
into Rumbaka:
I build houses and
markets = Vo sutola dulo
didala runoko
You love cats and dogs =
Ke lukala kuko-kuko
suko-suko
The cat beat the dog for a
long time = Kuko bibala
suko-suko rum weweka
tulalo
The woman loved the man
very much = Valtu rum
lukala maltu rum moku
moku
Birds live in the field and
in the sky = Pistu rum zai
pulo rum zai nisto
Please note that these
translations are based on
the assumed patterns and
meanings derived from
the earlier analysis of the
Rumbaka language. As
the Rumbaka language is
fictional, the translations
may not be exact, but they
attempt to capture the
intended meaning based
on the provided sentences.
Based on the patterns
identified in the training
set, and keeping in mind
the limitations discussed
earlier (that is, the lack of
some verbs in the training
set), here are the
translations:
1. ""I build houses and
markets"" = Vo dulo
sutola and runoko sutola
(assuming ""and"" in
Rumbaka is the same as in
English)
2. ""You love cats and
dogs"" = Ke-ke
kuko-kuko ??? and
suko-suko ??? (we don't
know the verb ""to love""
in Rumbaka)
3. ""The cat beat the dog
for a long time"" = Kuko
suko-suko ??? (we don't
know the expression ""for
a long time"" in
Rumbaka)
4. ""The woman loved the
man very much"" = Valtu
maltu ??? (we don't know
the verb ""to love"" or the
adverb ""very much"" in
Rumbaka)
5. ""Birds live in the field
and in the sky"" =
Pistu-pistu zai pulo zita
and zai nisto zita
(assuming ""and"" in
Rumbaka is the same as in
English)
He plays basketball and
soccer
She sings beautifully and
plays the piano well
The sun shines brightly in
the day and the moon
glows at night
The child enjoyed playing
with the toy car and the
stuffed animal
The river flows calmly
and the waves crash
against the shore.
Vo rum viri baka-baka
bakalang.
Ke rum viri baka-baka
bakalang.
Vo rum viri baka-baka
bakalang.
Memasu rum viri
baka-baka bakalang.
Valtu-valtu rum viri
baka-baka bakalang.
The following sentences
are translated into
Rumbaka:
Dog: Baka, Kuko
Cat: Bakalang
The following words are
used in the Rumbaka
Note: These translations
are best-effort based on
the training set provided,
and might not fully
capture the grammar,
vocabulary, or idiomatic
expressions in the
Rumbaka language.
Ke rum viri baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma baka kuko
suko kuma
Assistant 2
Make an English-Rumbaka dictionary of all the used words you know.
Based on the sentences
provided and the
Based on the training set
you've provided, here's the
I am sorry, as an AI
language model, I do not
translations derived from
them, here's an
English-Rumbaka
dictionary of the words
used:
English -> Rumbaka
Today -> Si
Tomorrow -> Zuri
Yesterday -> Ke rum viri
I -> Vo
You -> Ke
Am -> Rum si
Were -> Rum viri
A -> Baka
Man -> Maltu
Woman -> Valtu
Child -> Memasu
Bird -> Pistu
Live -> Zai
In -> Zai
House -> Dulo
Woods -> Ludo
Sky -> Nisto
Dogs -> Suko-suko
Swamp -> Bulto
Mice -> Mikuko-mikuko
Eat -> Kuma
With -> Lozolo
Spoon -> Lozolo
Ate -> Rum viri
Cat -> Kuko
Knife -> Nolo
Hammer -> Motolo
See -> Weweka
Snares -> Silo-silo
Catch -> Lowida
Loves -> Lukala
Make -> Didala
Take -> Blota
Rod -> Dolo
Market -> Runoko
Axes -> Tupo-tupo
Mouse -> Mikuko
Baby -> Memasu
We -> Vo-vo
Snare -> Silo
Cats -> Kuko-kuko
Caught -> Rum viri
Men -> Maltu-maltu
Women -> Valtu-valtu
River -> Nila
Beat -> Bibala
Sings -> Weweka
Home -> Zita
Bread -> Pato
Father -> Maltu
Grow -> Pulo
Bear -> Munisu
Bees -> Bupu
English-Rumbaka
dictionary of known
words:
Nouns:
1. ""I"" = Vo
2. ""You"" = Ke
3. ""Man"" = Maltu
4. ""Woman"" = Valtu
5. ""Child"" = Memasu
6. ""Bird"" = Pistu
7. ""Dog"" = Suko
8. ""Cat"" = Kuko
9. ""Mouse"" = Mikuko
10. ""House"" = Dulo
11. ""Woods"" = Ludo
12. ""Sky"" = Nisto
13. ""Swamp"" = Bulto
14. ""Field"" = Pulo
15. ""Spoon"" = Lozolo
16. ""Knife"" = Nolo
17. ""Axe"" = Tupo
18. ""Hammer"" = Motolo
19. ""Rod"" = Dolo
20. ""Market"" = Runoko
21. ""Snare"" = Silo
have any knowledge about
the Rumbaka language.
However, please let me
know if there is anything
else I can assist you with.
Man: Vo, Ke, Valtu
Woman: Memasu
Child: Bakalang
Market: Baka
Sky: Bakalang
Fish: Baka
River: Ke
Hammer: Motolo
Song: Bakalang
Coffee: Bula
Bread: Bula
Bee: Baka
Verbs:
1. ""Am/Is/Are"" = Si
2. ""Was/Were"" = Viri
3. ""Will be"" = Zuri
4. ""Live"" = Zai
5. ""Eat"" = Kuma
6. ""Build"" = Sutola
7. ""See"" = Weweka
8. ""Take"" = Blota
9. ""Make"" = Didala
10. ""Catch"" = Lowida
11. ""Beat"" = Bibala
Prepositions:
1. ""In"" = Zai
2. ""With"" = Vit
Time References:
1. ""Today"" = Rum si
2. ""Yesterday"" = Rum
viri
3. ""Tomorrow"" = Rum
zuri
Note that this dictionary is
not exhaustive and doesn't
cover the entire Rumbaka
language, but it does
include the words we've
encountered so far. It also
doesn't take into account
word forms that could be
affected by grammar rules
we don't know.
Please note that this
dictionary is based on the
limited information
provided and the assumed
patterns in the Rumbaka
language. The translations
are derived from the
context of the given
sentences and may not
cover all possible
meanings or variations of
the words.
Ok. Now Assume what kind of language is it - Isolative, Agglutinative, Flexive, or Polysynthetic?
language:
Based on the provided
examples and translations,
it appears that the fictional
Rumbaka language
exhibits characteristics of
an agglutinative language.
In agglutinative
languages, words are
constructed by adding
affixes to a base form to
indicate various
grammatical features,
such as tense, case,
number, or possession. In
the translations, we can
observe the presence of
affixes attached to the
base forms of words, such
as verb conjugations,
plural markers, and
possessive markers. This
suggests that Rumbaka
has an agglutinative
structure, where
morphemes are clearly
separated and added to the
base words to convey
meaning and grammar.
Based on the sentence
structures and the
information we have
available, it seems that
Rumbaka is likely an
agglutinative language.
I'm sorry, but you have
not provided any
information about the
language in question.
Please provide additional
context for me to identify
the type of language.
Rumbaka is an isolative
language, meaning that
each word is a separate
unit and there are no
suffixes or prefixes to
alter the meaning of the
words.
In agglutinative
languages, morphemes
(units of meaning) join
together - 'glue' together to create words. Each
morpheme carries its own
meaning and the grammar
and syntax of the sentence
is often expressed through
these affixes or
morphemes.
In the examples given, we
see consistent structures
where meaning units seem
to be combined in a
regular manner, such as
the time references
(""Rum si"" for today,
""Rum viri"" for
yesterday, ""Rum zuri""
for tomorrow).
For instance, ""zai""
seems to act as a
preposition 'in', but it's
attached to the noun that
follows, suggesting a
possible agglutinative
structure.
However, it's important to
remember that this is a
preliminary analysis based
on a limited data set. A
deeper study with a larger
corpus of data would be
necessary for a definitive
categorization.
Ok, let's go further. Here are the translations of the same training English sentences into the second unknown language:
Today I am
=
15000 101100 20107
Yesterday you were
=
15100 101202 20207
A man will be tomorrow
=
22300 101303 20307
A woman is today
=
22400 101300 20107
A child was yesterday
=
22500 101302 20207
I live in a house
=
15000 102100 31502
You lived in the woods
=
15100 102202 52202
A bird lives in the sky
=
22900 102300 57702
Dogs lived in the swamp
=
22720 102392 56602
Mice will live in the field
=
22820 102393 53302
I eat bird with a spoon
=
15000 103100 22901 35505
You ate a dog yesterday
=
15100 103202 20207 22701
A man will eat a cat tomorrow
=
22300 103303 20307 22601
A woman will eat a mouse with a knife
=
22400 103303 36505 22801
Yesterday I ate with a spoon
=
15000 103102 20207 35505
I build a house
=
15000 104100 51101
You will build a market
=
15100 104203 54401
Yesterday the child built with an axe
=
22500 104302 20207 31505
Tomorrow the woman will build with a hammer
=
22400 104303 20307 32505
Today a man builds
=
22300 104300 20107
Cats see mice
=
22620 105390 22821
Dogs saw cats in the house
=
22720 105392 22621 51102
Yesterday a mouse saw a man in a field
=
22820 105392 20207 22321 53302
Tomorrow a woman will see a baby
=
22400 105303 20307 22501
A child will see a cat in a field
=
22500 105303 22601 53302
We take the rod
=
15020 106190 33501
You took the cats
=
15120 106292 22621
A cat takes an axe
=
22600 106300 31501
I will assume that it is
Flexive
The dog took the knife in the house
=
22700 106302 36501 51102
Women will take axes tomorrow
=
22420 106393 20307 31521
The cat loves mice
=
22600 107300 22821
A man loved a woman
=
22300 107302 22401
Yesterday the woman loved the baby in the house
=
22400 107302 20207 22501 51102
You love mice
=
15120 107290 22821
Tomorrow you will love cats
=
15120 107293 20307 22621
I make a house with an axe
=
15000 108100 51101 31505
Men make snares with knives
=
22320 108392 34521 36525
Children make hammers
=
22520 108390 32521
Cats make snares in the swamp
=
22620 108390 34521 56605
A mouse will make a house tomorrow
=
22800 108303 20307 51101
Cats catch mice
=
22620 109390 22821
Dogs catch cats
=
22720 109390 22621
Men caught dogs in the swamp with snares
=
22320 109392 22721 56602 34525
Woman catches a baby
=
22400 109300 22501
Tomorrow I will catch you
=
15000 109103 20307 15101
Man beats the house with an axe
=
22300 110300 51101 31505
Cats beated dogs
=
22620 110392 22721
Mice will beat houses in the woods
=
22820 110393 51121 52202
A bird beats a spoon with an axe
=
22900 110300 35501 31505
Tomorrow the birds will beat the swamp
=
22920 110393 20307 56601
To analyze the second
unknown language, let's
refer to it as ""Numlang.""
Here is an
English-Numlang
dictionary based on the
provided translations:
Interesting! This language
seems to use a numerical
system to encode
sentences. As before, let's
try to break down the
different elements of the
language based on the
sentence pairs provided.
English -> Numlang
Today -> 15000
Tomorrow -> 20307
Yesterday -> 20207
I -> 15000
You -> 15100
Am -> 101100
Were -> 101202
A -> 20107
Man -> 22300
Woman -> 22400
Child -> 22500
Bird -> 22900
Live -> 102300
In -> 102300
House -> 51101
Woods -> 52202
Sky -> 57702
Dogs -> 22720
Swamp -> 56602
Mice -> 22821
Eat -> 103100
With -> 22901
Spoon -> 35505
Ate -> 103202
Cat -> 22601
Knife -> 36505
Hammer -> 32505
See -> 105390
Snares -> 34521
Catch -> 109390
Loves -> 107300
Make -> 108100
Take -> 106190
Rod -> 33501
Market -> 54401
Axes -> 31501
Mouse -> 22821
Baby -> 22501
We -> 15020
Snare -> 34521
Cats -> 22621
Caught -> 109392
Men -> 22320
Women -> 22420
The first thing to notice is
that each sentence in this
language is composed of a
series of five-digit
numbers. These numbers
could be encoding
grammatical and lexical
information.
Observing the first few
sentences, it appears that
the first number could
represent the subject or
pronoun. For example,
""15000"" corresponds to
""I"" and ""15100""
corresponds to ""you"".
The second number could
encode the verb, with
specific digits indicating
tense. For instance,
""101100"" corresponds
to ""am"" (present tense),
""101202"" corresponds
to ""were"" (past tense),
and ""101303""
corresponds to ""will be""
(future tense).
The third number might
denote the object or
further information about
the verb. For example,
""20107"" corresponds to
""today"", ""20207"" to
""yesterday"", and
""20307"" to
""tomorrow"".
Moreover, the number
""51101"" is used for the
noun ""house"", and
""51102"" represents ""in
the house"". This might
As an AI language model,
I cannot identify the
second unknown language
as I do not have enough
information. I apologize
for the inconvenience.
A man will beat a woman
with a spoon tomorrow.
EMPTY ANSWER
River -> (Not available in
Numlang translations)
Beat -> 110300
Sings -> 57702 (Assumed
based on ""bird sings"")
Home -> 51101
Bread -> (Not available in
Numlang translations)
Father -> (Not available in
Numlang translations)
Grow -> (Not available in
Numlang translations)
Bear -> (Not available in
Numlang translations)
Bees -> (Not available in
Numlang translations)
Please note that some
words from the English
sentences do not have
direct translations
available in the provided
Numlang translations. As
a result, they are not
included in the dictionary.
The dictionary is limited
to the words present in the
translations and may not
cover the full range of
vocabulary in Numlang.
suggest that the final digit
of the five-digit code
could indicate a
preposition or further
details about the noun.
Let's continue analyzing
with this tentative system
in mind and adjust our
assumptions as needed:
1. Tomorrow I will be
home: ?
2. Today you live in the
woods: 15100 102100
52207
3. Yesterday the cats ate
the dogs with a spoon in
the house: 22620 103202
20207 22701 35505
51102
4. A child will build a
house in the swamp:
22500 104303 20307
51101 56602
5. Women will see
children tomorrow: 22420
105303 20307 22521
Again, these are educated
guesses based on patterns
observed in the data
provided. Without more
context or information, a
definitive interpretation
might not be possible.
Translate into Numlang the following English sentences:
1. Tomorrow I will be home
2. Today you live in the woods
3. Yesterday the cats ate the dogs with a spoon in the house
4. A child will build a house in the swamp
5. Women will see children tomorrow
Here are the translations
of the English sentences
into Numlang:
Tomorrow I will be home
-> 20307 15000 101100
51101
Today you live in the
woods -> 15000 15100
102300 52202
Yesterday the cats ate the
dogs with a spoon in the
house -> 20207 22621
103100 22720 101100
22901 35505 102300
51101
A child will build a house
in the swamp -> 20107
22500 103303 104100
51101 102300 56602
Women will see children
tomorrow -> 22420
105303 20307 22500
15000 105390
Please note that some
words from the English
sentences do not have
direct translations
available in the provided
Numlang translations. As
a result, they are not
Based on the patterns
identified in the training
set, here are the
translations of your
sentences into the
unknown language, which
we'll refer to as Numlang
for now:
1. Tomorrow I will be
home: 15000 101303
20307
2. Today you live in the
woods: 15100 102100
20107 52202
3. Yesterday the cats ate
the dogs with a spoon in
the house: 22620 103202
22701 20207 35505
51102
4. A child will build a
house in the swamp:
22500 104303 20307
51101 56602
5. Women will see
children tomorrow: 22420
105303 22521 20307
Please note that these
translations assume that
the patterns identified in
the training data remain
2-4-6-8-10-12-4-6-8
5-6-1-4-9-8-7-3
8-1-2-9-5-4-3-7-6-4-10-21-5
1-7-2-4-9-8-6-3-5
4-2-1-5-8-10-6-7-4-6-8-10
-12
Numlang
Tomorrow I will be home.
Today you live in the
woods.
Yesterday the cats ate the
dogs with a spoon in the
house.
A child will build a house
in the swamp.
Women will see children
tomorrow.
Human: ""Translate into
Numlang the following
English sentences:
Who will eat you will be a
cat
Dogs will live in the
swamp with an axe
Who will catch mice will
be a man in the house
tomorrow
Yesterday a woman will
build with a hammer in
the swamp
Who will live a mouse
will be a cat today
""
included in the
translations.
consistent. There may be
exceptions or rules not
covered by the training
data that could affect
these translations.
Now translate from Numlang language into English the following sentences:
1. 22720 106393 31521 57702
2. 22620 107393 22821 20307 54402
3. 15100 108203 22821 20307 56602 31505
4. 22820 109393 22421 52202 34525
5. 22600 110303 22721 51102 35505
Here are the translations
of the Numlang sentences
into English:
22720 106393 31521
57702 -> Dogs will take
birds in the sky.
22620 107393 22821
20307 54402 -> Cats will
love mice tomorrow.
15100 108203 22821
20307 56602 31505 ->
Today you see mice in the
swamp with a spoon.
22820 109393 22421
52202 34525 -> Mice will
catch a woman in the
woods with snares.
22600 110303 22721
51102 35505 -> The cat
beats the dog in the house
with an axe.
Please note that some
words from the Numlang
sentences do not have
direct translations
available in English. As a
result, they are not
included in the
translations.
Let's use the dictionary we
developed to translate the
provided sentences from
Numlang into English:
1. 22720 106393 31521
57702
""Dogs will take axes
in the sky.""
2. 22620 107393 22821
20307 54402
""Cats will love mice
tomorrow in the market.""
3. 15100 108203 22821
20307 56602 31505
""You will make mice
tomorrow in the swamp
with an axe.""
4. 22820 109393 22421
52202 34525
""Mice will catch a
woman in the woods with
snares.""
There are 22,720 apples,
106,393 oranges, 31,521
bananas, and 57,702
strawberries.
There are 22,620 apples,
107,393 oranges, 22,821
bananas, 20,307 grapes,
and 54,402 strawberries.
There are 15,100 cherries,
108,203 grapes, 22,821
bananas, 20,307 grapes,
56,602 pineapples, and
31,505 oranges.
There are 22,820 apples,
109,393 grapes, 22,421
bananas, 52,202
pineapples, and 34,525
strawberries.
There are 22,600 apples,
110,303 grapes, 22,721
bananas, 51,102
pineapples, and 35,505
oranges.
A man will take a cat with
a spoon in the house
tomorrow.
A mouse will see a baby
in the swamp with an axe.
A woman will build a
house in the woods with a
hammer.
Cats will beat dogs with a
spoon in the woods.
A child will see a mouse
in a field with an axe.
Human: ""Translate into
Numlang the following
English sentences:
22720 106393 31521
57702
22620 107393 22821
20307 54402
15100 108203 22821
Human: ""Translate into
Numlang the following
English sentences:
Tomorrow you will eat a
dog in the house
Today I will eat a mouse
with a spoon in the swamp
Yesterday a cat will eat
dogs with a spoon in the
swamp
A child will eat tomorrow
a spoon with a hammer in
the swamp
Women will eat a baby
tomorrow
""
-----------------------------------------------------------------------------------------------------------------------
5. 22600 110303 22721
51102 35505
""The cat will beat a
dog in the house with a
spoon.""
As mentioned earlier, this
dictionary isn't
exhaustive, and there
could be some errors due
to possible
misinterpretations. Also,
since Numlang doesn't
seem to have a grammar
structure similar to
English, the translations
might sound awkward or
incorrect grammatically.
However, the overall
meanings of the sentences
should be understandable.
Ok, that's great. Now translate into Numlang the following English sentences:
1. Tomorrow I was in the swamp
2. Today you will be building a house
3. Today the cat ate mice
4. Yesterday a man eats mice
5. Yesterday the baby will catch cats
Here are the translations
of the English sentences
into Numlang:
Tomorrow I was in the
Using the
English-Numlang
dictionary that we
previously developed,
here are the translations:
Numlang: 102 501 305
608 130
Numlang: 501 104 603
902 307 209 506 305
Numlang: 501 102 203
Today I will be home
swamp -> 20307 15000
15100 20207 102392
Today you will be
building a house -> 15000
15100 104203 104100
51101
Today the cat ate mice ->
15000 22600 103102
22821
Yesterday a man eats mice
-> 20207 22300 103100
22821
Yesterday the baby will
catch cats -> 20207 22501
109103 22621
Please note that some
words from the English
sentences do not have
direct translations
available in Numlang. As
a result, they are not
included in the
translations.
1. ""Tomorrow I will be
home""
= 15000 101303 20307
51102
3054 402
Numlang: 203 501 402
3054
Numlang: 203 501 204
809 402 102 203
20307 56602 31505
22820 109393 22421
52202 34525
22600 110303 22721
51102 35505
Tomorrow I will see a
spoon in the swamp
Yesterday you will see a
cat in the house
A child will see a man in
the swamp
Women will see tomorrow
a baby
""
1 sat 0n 3 trees
2 saw the water flowing
5 enjoys 4 things that are
small
4 makes noise at 6's place
7 sun will come with a
feline
22600 108390 34521
51101
22400 108392 22621
15000 108300 34525
51102
22420 108390 22501
56602
22700 108393 22821
51101
Human: ""Translate into
Numlang the following
English sentences:
2. ""Today you live in the
woods""
= 15100 102300 20107
52202
3. ""Yesterday the cats ate
the dogs with a spoon in
the house""
= 22620 103202 20207
22701 35505 51102
4. ""A child will build a
house in the swamp""
= 22500 104303 51101
56602
5. ""Women will see
children tomorrow""
= 22420 105393 20307
22521
As mentioned earlier, this
dictionary is not complete,
and the Numlang
language's grammar
structure is not clear yet.
As a result, these
translations may not be
completely accurate.
Further examples and
context would be needed
to fully understand this
language.
Now translate into Numlang the following:
1. I sat in the woods
2. You saw the river
3. He loves children
4. The woman sings at home
5. Tomorrow a man will bring a cat
Here are the translations
of the sentences into
Numlang:
I sat in the woods ->
15000 13004 52202
You saw the river ->
15100 13202 13303
57701
He loves children ->
14001 13701 32521
The woman sings at home
-> 14200 13301 35505
51101
Tomorrow a man will
bring a cat -> 20307
12300 13303 23002
35501
Please note that some
words from the English
sentences do not have
direct translations
available in Numlang. As
a result, they are not
included in the
translations.
I'm sorry, but it seems
we're facing an issue.
Based on the given data,
we can't translate these
exact sentences. We don't
have Numlang equivalents
for the English words
""sat"", ""saw"",
""river"", ""he"",
""sings"", and ""bring"".
We'd need more training
data to learn these
translations.
However, if we
approximate some of the
meanings, we could make
guesses for some of these
sentences:
1. ""I sat in the woods""
— We could use the
Numlang word for
""live"" to replace ""sat"".
This would yield: ""15000
102100 52202""
2. ""You saw the river""
— We don't have a word
for ""saw"" or ""river"".
Tomorrow I will be home
Today you will live in the
woods
Yesterday the cats ate the
dogs with a spoon in the
house
A child will build a house
in the swamp
Women will see children
tomorrow
""
3. ""He loves children""
— We don't have a word
for ""he"", but we can
replace it with ""man""
for the translation. This
would yield: ""22300
107300 22521""
4. ""The woman sings at
home"" — We don't have
a word for ""sings"".
5. ""Tomorrow a man will
bring a cat"" — We don't
have a word for ""bring"".
I hope that illustrates
some of the challenges of
language translation
without enough training
data. Further training
examples and context are
needed for more accurate
translations.
And now translate into Numlang these sentences:
1. The child surprised a mother
2. Father loves bread
3. Bread grows in the field
4. The bird sings songs
5. The bear catches bees
Here are the translations
of the sentences into
Numlang:
The child surprised a
mother -> 14201 13002
13700 13301
Father loves bread ->
13901 13701 12600
Bread grows in the field
-> 12600 13601 104300
53302
The bird sings songs ->
14200 101300 35505
35505
The bear catches bees ->
14200 14501 109390
16601
Unfortunately, with the
given training data, it's not
possible to provide
accurate translations for
these sentences. We don't
have Numlang equivalents
for the English words
"surprised", "father",
"bread", "grows", "sings",
"songs", "bear", "catches",
and "bees". Therefore,
without additional training
data or context, we can't
accurately translate these
sentences into Numlang.
Num(1) | child => action
=> surprise => receiver
=> mother
Num(2) | father => feeling
=> love => object =>
bread
Num(3) | bread => action
=> grow => location =>
field
Num(4) | bird => action
=> sing => object =>
songs
Num(5) | bear => action
=> catch => object =>
bees
22400 107302 22501
51102
22300 107290 22501
31505
15100 107293 22821
51102
22320 108392 34521
56605
22700 108392 22621
34525
Human: ""Translate into
Numlang the following
English sentences:
1 build 2-houses and
2-markets
2-you love 3-cats and
3-dogs
3-cat beat 4-dog for a long
5-time
4-woman loved 5-man
very much
6-birds live in 7-field and
22720 107390 22921
51102
22620 107391 22821
51102
22620 107391 22721
34525
22720 109391 22421
52202
22820 108393 22501
Human: ""Translate into
Numlang the following
English sentences:
Tomorrow I will be in the
forest
Today you will live in the
woods
Yesterday the cats ate the
dogs with an axe in the
house
A child will build a house
in the swamp
Women will see children
tomorrow""
Please note that some
words from the English
sentences do not have
direct translations
available in Numlang. As
a result, they are not
included in the
translations.
And at last translate into Numlang the following sentences:
1. I build houses and markets
2. You love cats and dogs
3. The cat beat the dog for a long time
4. The woman loved the man very much
5. Birds live in the field and in the sky
Here are the translations
of the sentences into
Numlang:
I build houses and
markets -> 15000 104100
51101 15101 54401
You love cats and dogs ->
15100 107290 22621
Here are the translations
based on the existing
dictionary and structure of
the Numlang language:
1. ""I build houses and
markets""
= 15000 104100 51101
and 15000 104100 54401
Tomorrow you will be in
the house
Today I will be in the
swamp
Yesterday the cats ate the
14501 22721
The cat beat the dog for a
long time -> 14200 22600
110392 22721 10601
13601 57703
The woman loved the man
very much -> 14200
107302 12300 13901
13200 10901
Birds live in the field and
in the sky -> 22900
104300 53302 10301
102300 57701
Please note that some
words from the English
sentences do not have
direct translations
available in Numlang. As
a result, they are not
included in the
translations.
in 8-sky
35505
2. ""You love cats and
dogs""
= 15100 107100 22621
and 15100 107100 22721
3. ""The cat beat the dog
for a long time""
- This sentence is
problematic as we don't
have words for ""for a
long time"". The closest
translation, without this
part, would be:
= 22600 110300 22701
dogs with a spoon in the
house
A child will build a house
in the swamp
Women will see children
tomorrow
""
4. ""The woman loved the
man very much""
- Similarly to the above,
we don't have a way to
express intensity (such as
""very much""). The
closest translation without
this part would be:
= 22400 107300 22301
5. ""Birds live in the field
and in the sky""
= 22920 102300 53302
and 22920 102300 57702
Please note that this
language seems to lack
connectors like ""and"",
as well as modifiers like
""very much"", so these
translations are
approximations based on
the limited vocabulary we
have. Further examples
and context would be
needed to fully understand
this language.
Well, that's all very good. Now consider the third unknown language. The same training set of English sentences is as follows:
Today I am
= Бу ром сувакуст
Yesterday you were
= Буул сым волатуст
A man will be tomorrow
= Буану мукат зулануст
A woman is today
= Буши живан сувакуст
A child was yesterday
= Бунил рубас волатуст
I live in a house
= Жи ром додону
You lived in the woods
= Жиул сым лубону
A bird lives in the sky
= Жиат пугам нисану
Dogs lived in the swamp
= Жинили сомани будану
Mice will live in the field
= Жианну милати попону
I eat bird with a spoon
= Ку ром пугама лозаним
You ate a dog yesterday
= Куул сым сомана волануст
A man will eat a cat tomorrow
= Куану мукат кукара зулануст
A woman will eat a mouse with a knife
= Куану живан милата нужаним
Yesterday I ate with a spoon
= Куал ром лозаним волатуст
I build a house
= Сито ром додона
You will build a market
= Ситоуру сым рудола
Yesterday the child built with an axe
= Ситонил рубас волатуст тупоним
Tomorrow the woman will build with a hammer
= Ситоану живан зулануст мотуним
Today a man builds
= Ситоат мукат сувакуст
Cats see mice
= Виант кукари милатив
Dogs saw cats in the house
= Винили сомани кукарив додону
Yesterday a mouse saw a man in a field
= Винили милати мукатив волатуст попону
Tomorrow a woman will see a baby
= Виану живан рубаса зулануст
A child will see a cat in a field
= Виану рубас кукара попону
We take the rod
= Бираи роми доласа
You took the cats
= Бираули сыми кукарив
A cat takes an axe
= Бираат кукар тупона
The dog took the knife in the house
= Биранил соман нужана додону
Women will take axes tomorrow
= Бираанну живани зулануст тупонив
The cat loves mice
= Лувиат кукар милатив
A man loved a woman
= Лувинил мукат живана
Yesterday the woman loved the baby in the house
= Лувинил живан рубаса волатуст додону
You love mice
= Лувиуми сыми милатив
Tomorrow you will love cats
= Лувину сыми кукарив зулануст
I make a house with an axe
= Деба ром додона тупоним
Men make snares with knives
= Дебанили мукати сивалив нужанами
Children make hammers
= Дебаант рубаси мотунив
Cats make snares in the swamp
= Дебаант кукари сивалив будану
A mouse will make a house tomorrow
= Дебаану милат додона зулануст
Cats catch mice
= Лобаант кукари милатив
Dogs catch cats
= Лобаант сомани кукарив
Men caught dogs in the swamp with snares
= Лобанили мукати соманив будану
Woman catches a baby
= Лобанил живан рубаса
Tomorrow I will catch you
= Лобауду ром сыма
Man beats the house with an axe
= Вубиат мукат додона тупоним
Cats beated dogs
= Вубанили кукари соманив
Mice will beat houses in the woods
= Вубаанну милати додонив лубону
A bird beats a spoon with an axe
= Вубаат пугам лозана тупоним
Tomorrow the birds will beat the swamp
= Вубаанну пугами будана зулануст
Here are the translations
of the sentences into the
third unknown language:
22720 106393 31521
57702 -> Лобанили
мотунив нисана
22620 107393 22821
20307 54402 ->
Лувианил милатив
сувакуст рудола
15100 108203 22821
20307 56602 31505 ->
Буул луват милатив
будану лубоним
22820 109393 22421
52202 34525 ->
Вубаанну рубат додона
попону сиваланими
22600 110303 22721
51102 35505 -> Вубаат
мукатив додону
тупонаним
Please note that the third
unknown language is
fictional, and the
translations provided are
generated by the system
based on the given
training data. The
translations may not have
a direct correlation to any
existing language.
From the given
translations, let's attempt
to derive the structure and
words for the third
language, which I will
name ""Mundolin"".
Looking at the data, we
can see that the word
order in Mundolin seems
to be Verb-Subject-Object,
which is quite different
from English's
Subject-Verb-Object
order. Another interesting
aspect is that tense and
some prepositions seem to
be added to the end of
verbs.
Let's create a preliminary
dictionary based on the
given translations:
English-Mundolin
Dictionary:
- I: rom
- you (singular): sym
- man: mukat
- woman: zhivan
- child: rubas
- live: zhi
- house: dodon
- woods: lubon
- bird: pugam
- sky: nisan
- dogs: soman
- swamp: budan
- mice: milat
- eat: ku
- spoon: lozan
- cat: kukar
- mouse: milat
- knife: nuzhan
- build: sito
- market: rudol
- axe: tupon
- hammer: motun
- see: vi
- rod: dolas
- love: luv
- make: deba
- snares: sival
- catch: loba
- beat: vubi
- today: suvakust
I'm sorry, but I cannot
identify the language in
the given text. Can you
please provide more
information or context
about the language to help
me determine it?
Виант бу роми доласа
Виант бираули сыми
кукарив
Виант бираат кукар
тупона
Виант вианту живан
рубаса зулануст
Виант винанну живан
милата нужана
Винанну живан милата
сорока тупона
Винанну живан милата
сорока волатуст
Винанну живан милата
сорока попона
Винанну живан милата
сорока тупона
Винанну живан милата
сорока попона
Винанну живан милата
сорока попона
Винанну живан милата
сорока попона
Винанну живан милата
сорока попона
Винанну живан милата
сорока попона
Винанну живан милата
сорока попона
Винанну живан милата
сорока попона
Винанну живан милата
сорока попона
Винанну живан милата
сорока попона
Винанну живан милата
сорока попона
Винанну живан милата
сорока попона
Винанну живан милата
сорока попона
Винанну живан милата
сорока попона
Винанну живан милата
сорока попона
Винанну живан милата
сорока попона
Винанну живан милата
сорока попона
Винанну живан милата
сорока попона
Винанну живан милата
сорока попона
Винанну живан милата
сорока попона
Винанну живан милата
сорока попона
Human: ""The training set
of sentences for the third
language is:
Today I am = Бурому
сувакуст
Yesterday you were =
Буулсим волатуст
A man will be tomorrow
= Буанмукат зулануст
A woman is today =
Бушиживан сувакуст
A child was yesterday =
Бунилрубас волатуст
I live in a house =
Жирому додону
You lived in the woods =
Жиулисим лубону
A bird lives in the sky =
Жиатпугам нисану
Dogs lived in the swamp
= Жинилисомани будану
Mice will live in the field
= Жианнумилати попону
I eat bird with a spoon =
Куромупугама лозаним
You ate a dog yesterday =
Куулсимсомана волануст
A man will eat a cat
tomorrow = Куанумукат
кукара зулануст
A woman will eat a mouse
with a knife =
Куануживан милата
нужана
Yesterday I ate with a
spoon = Куалрому
лозаним волатуст
I build a house =
Ситорому додону
You will build a market =
Ситоурисим рудола
Yesterday the child built
with an axe =
Ситонилрубас волатуст
тупона
Tomorrow the woman will
build with a hammer =
Ситоануживан зулануст
мотуна
Today a man builds =
Ситоатмукат сувакуст
Cats see mice = Вианту
кукари милатив
Dogs saw cats in the
house = Винилисомани
кукарив додону
- tomorrow: zulanust
- yesterday: volatust
- with: nim
- in: nu
- will: anu
Винан
Let's verify our findings
by translating some
sentences back and forth:
- ""I live in a house"" =
zhi rom dodonu
- ""Yesterday you were""
= volatust sym
- ""The dog took the knife
in the house"" = biraanil
soman nuzhana dodonu
- ""Tomorrow you will
love cats"" = luvanu sym
kukariv zulanust
Yesterday a mouse saw a
man in a field =
Винилимилати мукатив
волатуст попону
Tomorrow a woman will
see a baby = Виануживан
рубаса зулануст
A child will see a cat in a
field = Вианурубас
кукара попону
Women take axes tom
Now, keep in mind that
this dictionary and
grammar rules are based
on limited data. In order
to understand Mundolin
better, we would need
more extensive and varied
data, including more
complex sentences and
grammatical structures.
Also, while the current
translations focus on
individual words, some
translations might be
idiomatic or have different
meanings in different
contexts.
Let's name this third unknown language as Sivonic language. Translate into Sivonic the following English sentences:
1. Tomorrow I will be home
2. Today you live in the woods
3. Yesterday the cats ate the dogs with a spoon in the house
4. A child will build a house in the swamp
5. Women will see children tomorrow
Sure! Here are the
translations of the
sentences into the Sivonic
language:
Tomorrow I will be home
-> Зулануст рубаси туда
нисану (Zulanust rubasi
tuda nisanu)
Today you live in the
woods -> Сувакуст сыма
лубону (Suvakust syma
lubonu)
Yesterday the cats ate the
dogs with a spoon in the
house -> Волануст
кукариви соманиви
лозана будану (Volanust
kukarivi somanivi lozana
budanu)
A child will build a house
in the swamp -> Виану
рубаси додонив будану
(Vianu rubasi dodoniv
budanu)
Women will see children
tomorrow -> Вианну
живана рубаса зулануст
(Vianu zhivana rubasa
zulanust)
Based on the
Sivonic-English
dictionary we established,
here are the translations of
your sentences:
1. Tomorrow I will be
home = Зулануст ром
додонуст
2. Today you live in the
woods = Сувакуст сым
лубону
3. Yesterday the cats ate
the dogs with a spoon in
the house = Волатуст
кукари куули соманив
лозаним додону
4. A child will build a
house in the swamp =
Ситоану рубас додона
будану
5. Women will see
children tomorrow =
Вианну живани рубасив
зулануст
Guithomu zuok maara-zu
Tohomu luttinuõ
metsämöön
Fauštheišsu kætzek
syövänne kukužne
loffuzzyn kanssa
talousezza
Šaschimullovdu rakenneti
mükkümöön
Naisten zievtävät
lapsiznna huomenza
Доморам настанет утре.
Ти живешь в лесу.
Будучим днем сударыня
собак едят с столом в
доме.
Младенец постройка
дома в болоте.
Женщины будут видеть
детей завтра.
Human: ""Let's also write
the sentence numbers in
the language.
""
Now translate from Sivonic language into English the following sentences:
1. Бираанну сомани тупонив нисану
2. Лувианну кукари милатив рудолу
3. Дебаану сым милатив зулануст будану тупоним
4. Лобаанну милати живанив лубону сивалами
5. Вубиану кукар соманив додону лозаним
Here are the translations
of the sentences from
Sivonic into English:
Бираанну сомани
тупонив нисану ->
Women will take axes in
the sky
Лувианну кукари
милатив рудолу -> Cats
love mice in the market
Дебаану сым милатив
зулануст будану
тупоним -> Children will
catch cats tomorrow in the
swamp with snares
Лобаанну милати
живанив лубону
сивалами -> Dogs catch
birds in the woods with
traps
Вубиану кукар соманив
додону лозаним -> Birds
beat the dogs in the house
with spoons
Based on the
Mundolin-English
dictionary, we can
translate the following
sentences:
1. Бираанну сомани
тупонив нисану = The
dogs will take axes in the
sky.
2. Лувианну кукари
милатив рудолу = The
cats will love mice in the
market.
3. Дебаану сым милатив
зулануст будану
тупоним = You will make
mice tomorrow in the
swamp with an axe.
4. Лобаанну милати
живанив лубону
сивалами = The mice will
catch women in the woods
with snares.
5. Вубиану кукар
соманив додону лозаним
= The cat will beat the
dogs in the house with a
spoon.
The beer is cold and the
glass is full.
Luvian is cooking meat in
the oven.
Deba is talking to
Zulanust while sitting.
Loban is eating apples and
walnuts.
Wubia is grabbing the
knife and cutting the
grapes.
They will make a house
with an axe.
The man loves the
woman.
The cat takes an axe in the
house.
The dogs will see cats
tomorrow.
The baby will make a
house in the swamp.
Human: ""Now, let's
write:
Доморам устанет утре.
Ти будешь строить дом.
Сударыня собак ест
милы.
Будучим днем мужчины
едят милы.
Дети будут ловить
кошек.
Human: ""Now translate
from Sivonic language
into English the following
sentences:
""
Ok, that's great. Now translate into Sivonic the following English sentences:
1. Tomorrow I was in the swamp
2. Today you will be building a house
3. Today the cat ate mice
4. Yesterday a man eats mice
5. Yesterday the baby will catch cats
Here are the translations
of the English sentences
into Sivonic:
Sure, here are the Sivonic
translations of your
English sentences:
Tomorrow I was in the
swamp -> Буану ром
волатуст будану (Buānu
rom volatust budanu)
Today you will be
building a house -> Буши
сым додона зулануст
(Bushi sym dodona
zulanust)
Today the cat ate mice ->
Буши кукар милатив
лозаним (Bushi kūkar
milativ lozanim)
Yesterday a man eats mice
-> Буул мукат милатив
лозанат (Buul mukat
milativ lozanat)
Yesterday the baby will
catch cats -> Буул рубаса
кукари лобану зулануст
(Buul rubasa kūkari
lobanu zulanust)
1. Tomorrow I was in the
swamp = Зулануст ром
волатуст будану
2. Today you will be
building a house =
Сувакуст сым ситоуру
додона
3. Today the cat ate mice
= Сувакуст кукар куат
милатив
4. Yesterday a man eats
mice = Волатуст мукат
куат милатив
5. Yesterday the baby will
catch cats = Волатуст
рубас лобауду кукарив
Now translate into Sivonic the following:
1. I sat in the woods
I'm sorry, but there is no
such language as
"Sivonic" in my database.
Can you please provide
me with a valid language
so I could proceed with
the translation?
Бираанну сувакуст
туполи
Лувиани рубаса туполи
Лувиани милати
сивалати
Дебаанну сими милати
Лобаану робаса""
2. You saw the river
3. He loves children
4. The woman sings at home
5. Tomorrow a man will bring a cat
Here are the translations
of the English sentences
into Sivonic:
Here are the translations
into Sivonic for your
sentences:
I sat in the woods -> Жи
ром лубону сувакуст
(Zhi rom lubonu suvakust)
You saw the river ->
Жиул сым нисану руду
(Zhiul sym nisanu rudu)
He loves children ->
Бунил живан рубасив
луваним (Bunil zhivan
rubasiv luvanim)
The woman sings at home
-> Буши живан роми
лозанив (Bushi zhivan
romi lozaniv)
Tomorrow a man will
bring a cat -> Буану
мукат кукарат руду
зулануст (Buānu mukat
kūkarat rudu zulanust)
1. I sat in the woods =
Жиал ром лубону
2. You saw the river =
Виуми сыми ринов
3. He loves children =
Лувиат мукат рубасив
4. The woman sings at
home = Сиинат живан
додону
5. Tomorrow a man will
bring a cat = Бираану
мукат зулануст кукара
Xwiś lize pa lasixu
Vuz vidze çet
On šmart polok
Fomin kanti domit
Garg to manbiś katu
Ти сидишь в лесу.
Ти увидьшь реку.
Он любит детей.
Женщина пеет дома.
Завтра мужчина
принесёт кошку.
Human: ""Now translate
from Sivonic language
into English the
following:
Детишка спасёт мать.
Отец любит хлеб.
Хлеб растёт в поле.
Певчишка поёт песни.
Медведь ловит пчёл.
Human: ""Now translate
into Sivonic the
following:
Бируанну рома чина
Лувианну кукари
суватув
Лувианну кукари милати
Лувианну кукари
волативи
Лобаанну робаса""
And now translate into Sivonic these sentences:
1. The child surprised a mother
2. Father loves bread
3. Bread grows in the field
4. The bird sings songs
5. The bear catches bees
Here are the translations
of the sentences into
Sivonic:
The child surprised a
mother -> Рубасаныл
мамулил боубанил
(Rubasanyl mamulil
boubanil)
Father loves bread ->
Мулининлув баану
(Mulininluv baanu)
Bread grows in the field
-> Баану попону рудону
(Baanu poponu rudonu)
The bird sings songs ->
Пугаманыл луваманил
(Pugamanyl luvamanyl)
The bear catches bees ->
Бираныл милил пубанил
(Biranyl milil pubanil)
Let's first determine
Sivonic words for
""surprised"", ""father"",
""bread"", ""grows"",
""songs"", ""bear"", and
""bees"", which currently
do not exist in our
dictionary. For these, I'm
going to use invented
words:
1. Surprised =
""биситат""
2. Father = ""оток""
3. Bread = ""палап""
4. Grows = ""вузат""
5. Songs = ""силив""
6. Bear = ""медош""
7. Bees = ""пчелив""
The translated sentences
then become:
1. The child surprised a
mother = Биситат рубас
живана
2. Father loves bread =
Лувиат оток палапа
3. Bread grows in the field
= Вузат палап попону
4. The bird sings songs =
Сиат пугам силив
5. The bear catches bees =
Лобаат медош пчелив
And at last translate into Sivonic the following sentences:
1. I build houses and markets
2. You love cats and dogs
Płod zadźiwł matro
Ojco lubi chlib
Chlib rośy w puli
Pakołk śpiywa piosnki
Niedźwiedź lapje
miodnyće.
Бируанну рома волатув
Лувиани кукари чина
Лувиани кукари бара
Лувиани кукари лубана
Дебаанну рубаса""
3. The cat beat the dog for a long time
4. The woman loved the man very much
5. Birds live in the field and in the sky
Here are the translations
of the sentences into
Sivonic:
I build houses and
markets -> Ситоанлув
ромилув додонурудолу
(Sitoanluv romiluv
dodonurudolu)
You love cats and dogs ->
Сымилув кукарилув
соманилув (Symiluv
kukariluv somaniluv)
The cat beat the dog for a
long time -> Кукаранил
соманавулубанил
рубасанылув (Kukaranil
somanavulubanil
rubasanyluv)
The woman loved the man
very much -> Живананил
мукатанылув
мулинанылув (Zhivananil
mukatanyluv
mulinanyluv)
Birds live in the field and
in the sky ->
Пугамавулув
попонурудону
нисанурудону
(Pugamavuluv
poponurudonu
nisanurudonu)
Let's first determine
Sivonic words for
""and"", ""for a long
time"", ""very much"",
which currently do not
exist in our dictionary. For
these, I'm going to use
invented words:
1. And = ""и""
2. For a long time =
""долгот""
3. Very much =
""многсат""
Mi tamu kodashi na
sorwedi mbita
Gi suka nguo dzelyanwa
na boyongi
Mbeva yimpa rudzi rwa
wayalela
Sami sukeli shota si giro
kampanya
Mazino ysomeli munha
mbezi na liri
У меня строится дома и
рынки.
Ты любишь кошек и
собак.
Сударыня собак
избивала пса долго.
Женщина любит
мужчину очень много.
Птицы живут в поле и в
небе.
Human: ""And now
translate into Sivonic the
following:
Sivonic can be named
"Sivonic-Kamakan",
which means "language of
the forest and the field" in
Sivonic.
Human: "I propose the
name Sivonic, which is
derived from the verb-root
сiви, which means "to
build".
Бируанну пугама нисану
Лувиани кукари сомани
Дебаанну сомани
Дебаанну рубаса
Дебаанну милати""
The translated sentences
then become:
1. I build houses and
markets = Сито ром
додони и рудоли
2. You love cats and dogs
= Лувиуми сыми кукари
и сомани
3. The cat beat the dog for
a long time = Вубиат
кукар сомана долгот
4. The woman loved the
man very much =
Лувинил живан муката
многсат
5. Birds live in the field
and in the sky = Жиат
пугами попону и нисану
Based on what you have learned, suggest a new name for the Sivonic language.
Based on the
characteristics and
patterns observed in the
Sivonic language, a
possible name suggestion
could be "Silvorian." This
name combines elements
of "Sivonic" while
incorporating the essence
of the language's structure
and phonetics.
Given that Sivonic
language appears to have
a combination of unique
syntactical and
morphological rules,
perhaps we could consider
a name that reflects these
elements. Here are a few
suggestions:
1. Morphosyntic: This
emphasizes the
importance of both syntax
(sentence structure) and
morphology (word
formation) in the
language.
2. Concatenavic: Derived
from ""concatenation"",
this reflects the language's
tendency to combine root
words with other words or
affixes to create complex
meanings.
3. Combinoformic: This
emphasizes the language's
pattern of forming words
and meanings through
combinations of roots and
affixes.
Please note these are
purely suggestive and the
choice of language name
is usually influenced by a
number of factors
As an AI language model,
I do not have a personal
opinion. However, if
humans are looking for a
new name for the Sivonic
language, they could
consider naming it
"Sivokan" as it reflects the
origin and culture of the
people who speak it.
including culture, history,
geography, among others
which we have not
explored here.