SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages

Gayane Ghazaryan, Erik Arakelyan, Pasquale Minervini, Isabelle Augenstein·June 20, 2024

Summary

The paper presents SynDARin, a method for generating and validating QA datasets in low-resource languages like Armenian. It uses parallel content mining from English and target languages to create synthetic multiple-choice questions, translating and validating them for quality. The process ensures content quality, factual accuracy, and reduces annotation costs. A 1.2K Armenian QA dataset is created, with high human evaluation scores, demonstrating the effectiveness of the translation validation. The dataset is used to benchmark LLMs, revealing their limitations in low-resource settings. The research contributes a novel framework, a high-quality Armenian QA dataset, and insights into LLM performance. Future work could expand to more languages and address translation challenges for rare languages.

Key findings

4

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the scarcity of Question Answering (QA) datasets for languages other than English, particularly in low-resource languages, by proposing a method called SynDARin for generating and validating QA datasets . This problem of limited QA datasets in non-English languages is not new, as the difficulty and cost associated with collecting and annotating such datasets have hindered the development and evaluation of multilingual Large Language Models (LLMs) . The proposed method focuses on creating QA datasets for low-resource languages by leveraging parallel content mining and synthetic question-answer pair generation techniques .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate a scientific hypothesis related to the construction of question-answering (QA) datasets in low-resource languages . The hypothesis revolves around the development of a novel method for producing QA datasets in low-resource languages that aims to overcome obstacles such as biases, hallucinations, and inconsistencies introduced during translation, cross-lingual transfer, or generation . The study focuses on creating a QA dataset for the Armenian language by mining parallel English and Armenian paragraphs from diverse Wikipedia articles and using automated translation and validation processes to ensure data quality . The research aims to provide a useful resource for measuring model performance in low-resource languages and evaluates the dataset's ability to challenge language models in zero-shot, few-shot, and fine-tuned modes .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages" proposes several innovative ideas, methods, and models in the field of Question Answering (QA) dataset creation for low-resource languages . Here are the key contributions outlined in the paper:

  1. Method for QA Dataset Construction: The paper introduces a novel method for generating and validating QA datasets for low-resource languages, specifically focusing on the Armenian language . This method involves utilizing parallel content mining to extract human-curated paragraphs in both English and the target language, then generating synthetic multiple-choice question-answer pairs based on the English data as context .

  2. QA Dataset Creation: The proposed method results in the creation of a QA dataset with 1.2K samples for the Armenian language . The dataset is designed to maintain content quality, reduce factual errors, and avoid the need for costly manual annotation .

  3. Evaluation of State-of-the-Art LLMs: The paper benchmarks state-of-the-art Large Language Models (LLMs) using the generated Armenian QA dataset . The evaluation shows that even very large models struggle to solve the dataset trivially, indicating its value as a challenging benchmarking resource .

  4. Addressing Limitations: While the proposed methods have been tested on a smaller-scale QA dataset in Armenian, the paper acknowledges the need for further analysis in more low-resource languages . The study suggests extending benchmarks to include a wider range of languages and addressing challenges in automatic translation for extremely rare languages .

In summary, the paper introduces an innovative approach to QA dataset creation for low-resource languages, specifically focusing on Armenian, and provides insights into evaluating LLMs on challenging datasets, highlighting the importance of addressing limitations for broader cross-lingual studies . The paper "SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages" introduces several key characteristics and advantages of its proposed method compared to previous approaches in the field of Question Answering (QA) dataset creation for low-resource languages .

  1. Innovative QA Dataset Construction Method: The paper presents a novel method for constructing QA datasets in low-resource languages, specifically focusing on Armenian. This method involves parallel data mining to extract human-curated paragraphs in both English and Armenian, ensuring content alignment by comparing their relative lengths .

  2. Quality Assurance and Evaluation: The proposed method includes ablations to demonstrate the quality of the generated samples and evaluates several Large Language Model (LLM) families on the QA dataset. The study shows that even very large models struggle to solve the dataset trivially, highlighting its value as a benchmarking resource .

  3. Benchmarking State-of-the-Art LLMs: The paper benchmarks several State-of-the-Art (SOTA) LLMs on the created Armenian QA dataset in supervised fine-tuning, zero-shot, and few-shot settings. The evaluation aims to assess if the dataset suffers from statistical biases or degenerate solutions, showing that the dataset is unlikely to have inconsistencies or degenerate solutions .

  4. Addressing Translation Challenges: Unlike previous methods that rely on direct machine translation or multilingual synthetic data generation, which may introduce biases and hallucinations, the proposed method circumvents these issues. It focuses on mining parallel English and Armenian paragraphs to create high-quality QA datasets without the limitations of traditional translation approaches .

  5. Human Evaluation and Validation: The paper employs human evaluation to ensure data quality, with human annotators inspecting samples to verify sufficient details for answering questions accurately. Additionally, automatic translation and validation processes are utilized to produce high-quality datasets for the Armenian language .

In summary, the proposed method in the paper offers a more robust and reliable approach to QA dataset creation in low-resource languages, addressing challenges related to translation biases, dataset quality, and evaluation of LLMs, thus providing a valuable contribution to the field of automated reasoning in low-resource languages .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

In the field of automated reasoning in low-resource languages, there are several related research works and notable researchers:

  • Noteworthy researchers in this field include Gayane Ghazaryan, Erik Arakelyan, Pasquale Minervini, and Isabelle Augenstein .
  • Other researchers who have contributed to related research include Marius Mosbach, Maksym Andriushchenko, Dietrich Klakow, Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, Benjamin Van Durme, Nils Reimers, Iryna Gurevych, Arij Riabi, Thomas Scialom, Rachel Keraron, Benoît Sagot, Djamé Seddah, Jacopo Staiano, among others .

The key solution mentioned in the paper is the proposed method called SynDARin, which focuses on generating and validating question-answering (QA) datasets for low-resource languages. This method involves utilizing parallel content mining to obtain human-curated paragraphs in English and the target language, generating synthetic multiple-choice question-answer pairs using English data as context, automatically translating and validating the pairs, and combining them with non-English human-curated paragraphs to create the final QA dataset. This approach helps maintain content quality, reduces the likelihood of factual errors, and avoids costly annotation processes .


How were the experiments in the paper designed?

The experiments in the paper were designed with the following key components:

  • Methodology: The experiments involved a novel method for QA dataset construction in low-resource languages, a QA dataset in Armenian, ablations demonstrating the quality of generated samples, and an evaluation of several Large Language Model (LLM) families on the QA dataset .
  • Dataset Generation: The experiments mined and matched 300 parallel English and Armenian paragraphs from Wikipedia. English data was used as context to generate 10 diverse questions for each paragraph, creating 3000 English question-answer pairs .
  • Evaluation: Human evaluation was conducted to assess data quality, with a human annotator inspecting 50 randomly chosen samples from the English QA dataset. The evaluation showed that 98% of examples contained sufficient details to answer the question accurately capturing contextual information .
  • Translation and Validation: The experiments involved translating 3000 question-answer samples into Armenian and using a validation pipeline to produce 1234 filtered examples for the Armenian QA dataset. Native-speaking annotators evaluated the quality of the translation validation pipeline and the overall produced datasets .
  • Benchmarking: The experiments benchmarked several State-of-the-Art (SOTA) Large Language Models (LLMs) on the QA dataset in supervised fine-tuning, zero-shot, and few-shot settings. The dataset was tested to ensure it did not suffer from statistical biases or degenerate solutions .
  • Experimental Setup: The experiments involved training models on varying numbers of training samples and benchmarking them on the testing set of the Armenian QA dataset to explore dataset biases and degenerate solutions .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is an Armenian QA dataset . The code for the proposed method, SynDARin, is not explicitly mentioned to be open source in the provided context. However, the study details the methodology and results, which can potentially guide the development of similar approaches in generating QA datasets for low-resource languages .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study conducted a systematic evaluation of the data quality by involving human annotators to inspect samples from the English QA dataset, demonstrating that 98% of examples contained sufficient details to answer questions accurately . Additionally, the study employed native-speaking annotators to evaluate the translation validation pipeline and the overall produced datasets, revealing that 72% of the examples marked as poor during validation were unanswerable due to lacking context or translation issues . These evaluations indicate a rigorous approach to ensuring the quality and reliability of the generated datasets.

Furthermore, the paper discusses the translation and validation process, where the generated question-answer pairs in English were translated into Armenian using the Google Translate API. To maintain consistency and accuracy, only samples where the translated answer was semantically related to the paragraph in Armenian were retained, filtering out samples that did not meet the specified criteria . This meticulous validation process highlights the attention to detail and the effort put into ensuring the integrity of the dataset.

Moreover, the study benchmarked several state-of-the-art Large Language Models (LLMs) on the produced Armenian QA dataset in supervised fine-tuning, zero-shot, and few-shot settings. The results indicated that even the largest models did not trivially solve the dataset, emphasizing its utility as a benchmarking tool and showcasing the value of the proposed resource in evaluating QA reasoning capabilities in low-resource languages . This comprehensive evaluation of LLMs on the dataset further strengthens the scientific validity and relevance of the study's findings.

In conclusion, the experiments and results presented in the paper offer robust support for the scientific hypotheses that needed verification. The thorough evaluation of data quality, translation validation, and benchmarking of LLMs demonstrate a methodical and rigorous approach to dataset creation and analysis, contributing significantly to the advancement of automated reasoning in low-resource languages.


What are the contributions of this paper?

The paper presents several key contributions:

  • A novel method for constructing a question-answering dataset in low-resource languages .
  • The generation of multilingual question-answer pairs using large language models .
  • Translation of question-answer pairs and validation through answer substring and semantic matching in parallel paragraphs .
  • Evaluation of the dataset as a reasoning benchmark for Armenian, showcasing its value in measuring model performance .

What work can be continued in depth?

Further research in this area can be extended by conducting a more comprehensive analysis and exploration of the proposed methods for creating QA datasets in low-resource languages. This includes expanding the study benchmarks to encompass a wider range of multilingual, low-resource languages and analyzing them in more depth . Additionally, the automatic translation component within the pipeline could be further developed to enhance the translation quality and accuracy, especially for extremely rare low-resource languages .


Introduction
Background
Overview of low-resource language challenges in QA datasets
Importance of parallel content mining
Objective
To develop SynDARin: a novel method for QA dataset creation
Aim to improve factual accuracy and reduce annotation costs
Focus on Armenian as a case study
Method
Data Collection
Parallel Content Mining
English-Armenian parallel corpora extraction
Selection of relevant content for QA generation
Question Template Extraction
English QA dataset analysis
Identifying question patterns and answer types
Data Synthesis
Synthetic multiple-choice question generation
Integration of parallel content and question templates
Data Preprocessing and Translation
Translation from English to Armenian
Machine translation techniques
Post-editing for quality control
Fact Checking and Validation
Human-in-the-loop validation process
Ensuring content quality and factual accuracy
Dataset Creation
1.2K Armenian QA dataset production
Human evaluation for dataset quality
Experiments and Evaluation
Benchmarking LLMs
Using the Armenian dataset to assess LLM performance
Identifying limitations in low-resource settings
Human Evaluation
High evaluation scores and dataset quality assessment
Contributions
Novel framework for low-resource QA dataset generation
High-quality Armenian QA dataset
Insights into LLM performance in low-resource languages
Future Work
Expansion to more languages
Addressing translation challenges for rare languages
Continuous improvement of the methodology
Conclusion
Summary of key findings and implications
Potential applications and future research directions
Basic info
papers
computation and language
machine learning
artificial intelligence
Advanced features
Insights
What is the primary focus of SynDARin?
What insights does the research provide regarding LLMs in low-resource settings?
What is the significance of the 1.2K Armenian QA dataset created by SynDARin?
How does SynDARin generate QA datasets for low-resource languages?

SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages

Gayane Ghazaryan, Erik Arakelyan, Pasquale Minervini, Isabelle Augenstein·June 20, 2024

Summary

The paper presents SynDARin, a method for generating and validating QA datasets in low-resource languages like Armenian. It uses parallel content mining from English and target languages to create synthetic multiple-choice questions, translating and validating them for quality. The process ensures content quality, factual accuracy, and reduces annotation costs. A 1.2K Armenian QA dataset is created, with high human evaluation scores, demonstrating the effectiveness of the translation validation. The dataset is used to benchmark LLMs, revealing their limitations in low-resource settings. The research contributes a novel framework, a high-quality Armenian QA dataset, and insights into LLM performance. Future work could expand to more languages and address translation challenges for rare languages.
Mind map
Ensuring content quality and factual accuracy
Human-in-the-loop validation process
Post-editing for quality control
Machine translation techniques
Identifying question patterns and answer types
English QA dataset analysis
Selection of relevant content for QA generation
English-Armenian parallel corpora extraction
High evaluation scores and dataset quality assessment
Identifying limitations in low-resource settings
Using the Armenian dataset to assess LLM performance
Human evaluation for dataset quality
1.2K Armenian QA dataset production
Fact Checking and Validation
Translation from English to Armenian
Integration of parallel content and question templates
Synthetic multiple-choice question generation
Question Template Extraction
Parallel Content Mining
Focus on Armenian as a case study
Aim to improve factual accuracy and reduce annotation costs
To develop SynDARin: a novel method for QA dataset creation
Importance of parallel content mining
Overview of low-resource language challenges in QA datasets
Potential applications and future research directions
Summary of key findings and implications
Continuous improvement of the methodology
Addressing translation challenges for rare languages
Expansion to more languages
Insights into LLM performance in low-resource languages
High-quality Armenian QA dataset
Novel framework for low-resource QA dataset generation
Human Evaluation
Benchmarking LLMs
Dataset Creation
Data Preprocessing and Translation
Data Synthesis
Data Collection
Objective
Background
Conclusion
Future Work
Contributions
Experiments and Evaluation
Method
Introduction
Outline
Introduction
Background
Overview of low-resource language challenges in QA datasets
Importance of parallel content mining
Objective
To develop SynDARin: a novel method for QA dataset creation
Aim to improve factual accuracy and reduce annotation costs
Focus on Armenian as a case study
Method
Data Collection
Parallel Content Mining
English-Armenian parallel corpora extraction
Selection of relevant content for QA generation
Question Template Extraction
English QA dataset analysis
Identifying question patterns and answer types
Data Synthesis
Synthetic multiple-choice question generation
Integration of parallel content and question templates
Data Preprocessing and Translation
Translation from English to Armenian
Machine translation techniques
Post-editing for quality control
Fact Checking and Validation
Human-in-the-loop validation process
Ensuring content quality and factual accuracy
Dataset Creation
1.2K Armenian QA dataset production
Human evaluation for dataset quality
Experiments and Evaluation
Benchmarking LLMs
Using the Armenian dataset to assess LLM performance
Identifying limitations in low-resource settings
Human Evaluation
High evaluation scores and dataset quality assessment
Contributions
Novel framework for low-resource QA dataset generation
High-quality Armenian QA dataset
Insights into LLM performance in low-resource languages
Future Work
Expansion to more languages
Addressing translation challenges for rare languages
Continuous improvement of the methodology
Conclusion
Summary of key findings and implications
Potential applications and future research directions
Key findings
4

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the scarcity of Question Answering (QA) datasets for languages other than English, particularly in low-resource languages, by proposing a method called SynDARin for generating and validating QA datasets . This problem of limited QA datasets in non-English languages is not new, as the difficulty and cost associated with collecting and annotating such datasets have hindered the development and evaluation of multilingual Large Language Models (LLMs) . The proposed method focuses on creating QA datasets for low-resource languages by leveraging parallel content mining and synthetic question-answer pair generation techniques .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate a scientific hypothesis related to the construction of question-answering (QA) datasets in low-resource languages . The hypothesis revolves around the development of a novel method for producing QA datasets in low-resource languages that aims to overcome obstacles such as biases, hallucinations, and inconsistencies introduced during translation, cross-lingual transfer, or generation . The study focuses on creating a QA dataset for the Armenian language by mining parallel English and Armenian paragraphs from diverse Wikipedia articles and using automated translation and validation processes to ensure data quality . The research aims to provide a useful resource for measuring model performance in low-resource languages and evaluates the dataset's ability to challenge language models in zero-shot, few-shot, and fine-tuned modes .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages" proposes several innovative ideas, methods, and models in the field of Question Answering (QA) dataset creation for low-resource languages . Here are the key contributions outlined in the paper:

  1. Method for QA Dataset Construction: The paper introduces a novel method for generating and validating QA datasets for low-resource languages, specifically focusing on the Armenian language . This method involves utilizing parallel content mining to extract human-curated paragraphs in both English and the target language, then generating synthetic multiple-choice question-answer pairs based on the English data as context .

  2. QA Dataset Creation: The proposed method results in the creation of a QA dataset with 1.2K samples for the Armenian language . The dataset is designed to maintain content quality, reduce factual errors, and avoid the need for costly manual annotation .

  3. Evaluation of State-of-the-Art LLMs: The paper benchmarks state-of-the-art Large Language Models (LLMs) using the generated Armenian QA dataset . The evaluation shows that even very large models struggle to solve the dataset trivially, indicating its value as a challenging benchmarking resource .

  4. Addressing Limitations: While the proposed methods have been tested on a smaller-scale QA dataset in Armenian, the paper acknowledges the need for further analysis in more low-resource languages . The study suggests extending benchmarks to include a wider range of languages and addressing challenges in automatic translation for extremely rare languages .

In summary, the paper introduces an innovative approach to QA dataset creation for low-resource languages, specifically focusing on Armenian, and provides insights into evaluating LLMs on challenging datasets, highlighting the importance of addressing limitations for broader cross-lingual studies . The paper "SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages" introduces several key characteristics and advantages of its proposed method compared to previous approaches in the field of Question Answering (QA) dataset creation for low-resource languages .

  1. Innovative QA Dataset Construction Method: The paper presents a novel method for constructing QA datasets in low-resource languages, specifically focusing on Armenian. This method involves parallel data mining to extract human-curated paragraphs in both English and Armenian, ensuring content alignment by comparing their relative lengths .

  2. Quality Assurance and Evaluation: The proposed method includes ablations to demonstrate the quality of the generated samples and evaluates several Large Language Model (LLM) families on the QA dataset. The study shows that even very large models struggle to solve the dataset trivially, highlighting its value as a benchmarking resource .

  3. Benchmarking State-of-the-Art LLMs: The paper benchmarks several State-of-the-Art (SOTA) LLMs on the created Armenian QA dataset in supervised fine-tuning, zero-shot, and few-shot settings. The evaluation aims to assess if the dataset suffers from statistical biases or degenerate solutions, showing that the dataset is unlikely to have inconsistencies or degenerate solutions .

  4. Addressing Translation Challenges: Unlike previous methods that rely on direct machine translation or multilingual synthetic data generation, which may introduce biases and hallucinations, the proposed method circumvents these issues. It focuses on mining parallel English and Armenian paragraphs to create high-quality QA datasets without the limitations of traditional translation approaches .

  5. Human Evaluation and Validation: The paper employs human evaluation to ensure data quality, with human annotators inspecting samples to verify sufficient details for answering questions accurately. Additionally, automatic translation and validation processes are utilized to produce high-quality datasets for the Armenian language .

In summary, the proposed method in the paper offers a more robust and reliable approach to QA dataset creation in low-resource languages, addressing challenges related to translation biases, dataset quality, and evaluation of LLMs, thus providing a valuable contribution to the field of automated reasoning in low-resource languages .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

In the field of automated reasoning in low-resource languages, there are several related research works and notable researchers:

  • Noteworthy researchers in this field include Gayane Ghazaryan, Erik Arakelyan, Pasquale Minervini, and Isabelle Augenstein .
  • Other researchers who have contributed to related research include Marius Mosbach, Maksym Andriushchenko, Dietrich Klakow, Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, Benjamin Van Durme, Nils Reimers, Iryna Gurevych, Arij Riabi, Thomas Scialom, Rachel Keraron, Benoît Sagot, Djamé Seddah, Jacopo Staiano, among others .

The key solution mentioned in the paper is the proposed method called SynDARin, which focuses on generating and validating question-answering (QA) datasets for low-resource languages. This method involves utilizing parallel content mining to obtain human-curated paragraphs in English and the target language, generating synthetic multiple-choice question-answer pairs using English data as context, automatically translating and validating the pairs, and combining them with non-English human-curated paragraphs to create the final QA dataset. This approach helps maintain content quality, reduces the likelihood of factual errors, and avoids costly annotation processes .


How were the experiments in the paper designed?

The experiments in the paper were designed with the following key components:

  • Methodology: The experiments involved a novel method for QA dataset construction in low-resource languages, a QA dataset in Armenian, ablations demonstrating the quality of generated samples, and an evaluation of several Large Language Model (LLM) families on the QA dataset .
  • Dataset Generation: The experiments mined and matched 300 parallel English and Armenian paragraphs from Wikipedia. English data was used as context to generate 10 diverse questions for each paragraph, creating 3000 English question-answer pairs .
  • Evaluation: Human evaluation was conducted to assess data quality, with a human annotator inspecting 50 randomly chosen samples from the English QA dataset. The evaluation showed that 98% of examples contained sufficient details to answer the question accurately capturing contextual information .
  • Translation and Validation: The experiments involved translating 3000 question-answer samples into Armenian and using a validation pipeline to produce 1234 filtered examples for the Armenian QA dataset. Native-speaking annotators evaluated the quality of the translation validation pipeline and the overall produced datasets .
  • Benchmarking: The experiments benchmarked several State-of-the-Art (SOTA) Large Language Models (LLMs) on the QA dataset in supervised fine-tuning, zero-shot, and few-shot settings. The dataset was tested to ensure it did not suffer from statistical biases or degenerate solutions .
  • Experimental Setup: The experiments involved training models on varying numbers of training samples and benchmarking them on the testing set of the Armenian QA dataset to explore dataset biases and degenerate solutions .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is an Armenian QA dataset . The code for the proposed method, SynDARin, is not explicitly mentioned to be open source in the provided context. However, the study details the methodology and results, which can potentially guide the development of similar approaches in generating QA datasets for low-resource languages .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study conducted a systematic evaluation of the data quality by involving human annotators to inspect samples from the English QA dataset, demonstrating that 98% of examples contained sufficient details to answer questions accurately . Additionally, the study employed native-speaking annotators to evaluate the translation validation pipeline and the overall produced datasets, revealing that 72% of the examples marked as poor during validation were unanswerable due to lacking context or translation issues . These evaluations indicate a rigorous approach to ensuring the quality and reliability of the generated datasets.

Furthermore, the paper discusses the translation and validation process, where the generated question-answer pairs in English were translated into Armenian using the Google Translate API. To maintain consistency and accuracy, only samples where the translated answer was semantically related to the paragraph in Armenian were retained, filtering out samples that did not meet the specified criteria . This meticulous validation process highlights the attention to detail and the effort put into ensuring the integrity of the dataset.

Moreover, the study benchmarked several state-of-the-art Large Language Models (LLMs) on the produced Armenian QA dataset in supervised fine-tuning, zero-shot, and few-shot settings. The results indicated that even the largest models did not trivially solve the dataset, emphasizing its utility as a benchmarking tool and showcasing the value of the proposed resource in evaluating QA reasoning capabilities in low-resource languages . This comprehensive evaluation of LLMs on the dataset further strengthens the scientific validity and relevance of the study's findings.

In conclusion, the experiments and results presented in the paper offer robust support for the scientific hypotheses that needed verification. The thorough evaluation of data quality, translation validation, and benchmarking of LLMs demonstrate a methodical and rigorous approach to dataset creation and analysis, contributing significantly to the advancement of automated reasoning in low-resource languages.


What are the contributions of this paper?

The paper presents several key contributions:

  • A novel method for constructing a question-answering dataset in low-resource languages .
  • The generation of multilingual question-answer pairs using large language models .
  • Translation of question-answer pairs and validation through answer substring and semantic matching in parallel paragraphs .
  • Evaluation of the dataset as a reasoning benchmark for Armenian, showcasing its value in measuring model performance .

What work can be continued in depth?

Further research in this area can be extended by conducting a more comprehensive analysis and exploration of the proposed methods for creating QA datasets in low-resource languages. This includes expanding the study benchmarks to encompass a wider range of multilingual, low-resource languages and analyzing them in more depth . Additionally, the automatic translation component within the pipeline could be further developed to enhance the translation quality and accuracy, especially for extremely rare low-resource languages .

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