CaLMQA: Exploring culturally specific long-form question answering across 23 languages

Shane Arora, Marzena Karpinska, Hung-Ting Chen, Ipsita Bhattacharjee, Mohit Iyyer, Eunsol Choi·June 25, 2024

Summary

The CaLMQA dataset, a multilingual long-form QA resource in 23 languages, addresses the scarcity of non-English research. It reveals that large language models struggle with low-resource languages and culturally nuanced questions, particularly affecting Tswana, Tongan, and Afar. The study introduces the CALM-Score metric to evaluate performance. Human assessments show that models often lack factual accuracy, omit important details, and exhibit language inconsistencies, emphasizing the need for more research on multilingual LLMs and culturally diverse QA. The table comparing three AI models (CLAUDE-3-OPUS, GPT-4-TURBO, and MIXTRAL-8X22B) reveals factuality issues, with GPT-4-TURBO having more illogical and irrelevant responses, MIXTRAL-8X22B showing hallucinations and cultural missteps, and CLAUDE-3-OPUS excelling in culturally specific questions. Overall, the paper underscores the importance of cultural awareness in AI systems and the need for improved performance across languages.

Key findings

7

Paper digest

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

The paper attempts to address the challenge of culturally specific long-form question answering (LFQA) across 23 languages . This problem involves generating well-written, factual, and complete answers in various languages, which can be particularly challenging for low-resource languages due to the need for manual question writing and translation . The study focuses on the limitations and difficulties faced in producing high-quality answers in non-English languages, highlighting the struggles models encounter in generating accurate and fluent responses .

While the issue of multilingual LFQA is not entirely new, the paper sheds light on the complexities and limitations associated with generating culturally specific answers across a wide range of languages . The research emphasizes the need for comprehensive metrics to evaluate the overall quality of answers in multilingual LFQA, indicating that current language models still face challenges in producing accurate and fluent responses in languages other than English .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis that there is a significant increase in surface-level issues in model-generated answers to low-resource language questions, particularly those generated by LLAMA-3-70B and MIXTRAL-8X22B, as revealed through the evaluation on CALMQA using the new metric CALMSCORE . Additionally, the study highlights the necessity for more robust automatic evaluation metrics effective across multiple languages and emphasizes the need for enhanced multilingual instruction tuning to address the prevalence of responses primarily generated in English by certain models .


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

The paper "CaLMQA: Exploring culturally specific long-form question answering across 23 languages" introduces several new ideas, methods, and models . Some of the key contributions include:

  • Medexpqa: A multilingual benchmark for large language models in medical question answering .
  • Model Family: The Claude 3 Model Family consisting of Opus, Sonnet, and Haiku models .
  • GPT-4 Technical Report: Details about the GPT-4 model, its capabilities, and technical specifications .
  • Model Release Blog: Information about the GPT-4o model release by OpenAI .
  • Aya Model: An instruction finetuned open-access multilingual language model .
  • Llama 3 Model Card: Details about the Llama 3 model .
  • Cheaper, Better, Faster, Stronger: A technical report by Mistral AI .
  • GeoMLAMA: Geo-diverse commonsense probing on multilingual pre-trained language models .
  • PhotoshopQuiA: A corpus of non-factoid questions and answers for why-question answering .
  • MLQA: Evaluation of cross-lingual extractive question answering .
  • Mkqa: A linguistically diverse benchmark for multilingual open domain question answering .
  • Tydi QA: A benchmark for information-seeking question answering in typologically diverse languages .
  • Factscore: Fine-grained atomic evaluation of factual precision in long-form text generation .
  • Comparing Hallucination Detection Metrics: Evaluation of metrics for multilingual generation .
  • Hurdles to Progress in Long-form Question Answering: Challenges and obstacles in advancing long-form question answering .
  • Critical Evaluation of Evaluations for Long-form Question Answering: Assessment of evaluation methods for long-form question answering . The paper "CaLMQA: Exploring culturally specific long-form question answering across 23 languages" introduces several characteristics and advantages compared to previous methods. Here are some key points based on the details in the paper:
  1. Culturally Specific Question Answering: The paper focuses on culturally specific long-form question answering across 23 languages, which is a significant advancement compared to previous methods that may have been limited to specific languages or regions.

  2. Multilingual Benchmark: The introduction of the Medexpqa benchmark for large language models in medical question answering provides a standardized evaluation platform for assessing the performance of models across different languages and medical domains.

  3. Model Family: The Claude 3 Model Family, including Opus, Sonnet, and Haiku models, offers a range of models with varying capabilities and architectures, allowing for more flexibility and customization based on specific task requirements.

  4. GPT-4 Technical Report: The detailed technical report on the GPT-4 model provides insights into its architecture, training process, and performance metrics, enabling researchers and practitioners to better understand and utilize the model for various applications.

  5. Model Release Information: The paper includes information about the release of the GPT-4o model by OpenAI, highlighting the continuous development and deployment of state-of-the-art language models for the research community.

  6. Instruction Finetuned Model: The Aya model, which is instruction finetuned and open-access, offers a unique approach to training language models that can potentially improve performance on specific tasks or domains.

  7. Model Card: The Llama 3 Model Card provides essential details about the model's capabilities, limitations, and potential biases, promoting transparency and accountability in AI model development and deployment.

  8. Diverse Evaluation Benchmarks: The paper introduces various evaluation benchmarks such as MLQA, Mkqa, Tydi QA, and GeoMLAMA, which cover a wide range of languages and question types, enabling comprehensive assessment of model performance across different linguistic and cultural contexts.

Overall, the characteristics and advantages presented in the paper demonstrate a significant advancement in the field of long-form question answering, particularly in terms of cultural diversity, model transparency, and evaluation benchmarking compared to previous methods.


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 culturally specific long-form question answering, there are several related research papers and notable researchers mentioned in the provided context . Some of the noteworthy researchers in this field include Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, and many others .

One of the key solutions mentioned in the research papers is the development of benchmarks and datasets for culturally specific question answering, such as the "WikiHowQA" benchmark . These benchmarks aim to provide a comprehensive evaluation platform for multi-document non-factoid question answering, contributing to the advancement of culturally aware language technologies .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate seven state-of-the-art models on the CALMQA dataset using the new metric CALMSCORE. These experiments aimed to assess the performance of the models on 2.6K culturally specific or culturally agnostic questions covering 23 languages ranging from high- to low-resource settings. The results revealed a significant increase in surface-level issues in model-generated answers, particularly for low-resource language questions, with LLAMA-3-70B and MIXTRAL-8X22B showing challenges in processing such inputs. GEMINI-1.5-PRO encountered API errors when handling low-resource languages. Human evaluation was conducted on CLAUDE-3-OPUS, GPT-4-TURBO, and MIXTRAL-8X22B using a subset of CALMQA, showing that while CLAUDE-3-OPUS and GPT-4-TURBO performed well on culturally agnostic questions, CLAUDE-3-OPUS's performance degraded significantly on culturally specific questions .


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

The dataset used for quantitative evaluation in the study is CALMQA . The code for the data labeling software used in the project, Label Studio, is open source and available on GitHub .


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 in the paper provide valuable insights to support the scientific hypotheses that need to be verified. The study evaluates various aspects of question answering systems across different languages, highlighting both positive and negative feedback from annotators in terms of artificiality, fluency, and clarity of the answers provided . The results of the experiments, such as the analysis of different models and their impact on the responses, contribute to understanding the effectiveness and naturalness of the question answering process . The feedback from annotators regarding the structure of the answers, including the use of fact enumerations, sheds light on how these elements can influence the perceived human-likeness of the responses . Overall, the experiments offer a comprehensive evaluation of question answering systems in multiple languages, providing valuable insights for verifying scientific hypotheses related to the effectiveness and naturalness of such systems.


What are the contributions of this paper?

The paper "CaLMQA: Exploring culturally specific long-form question answering across 23 languages" makes several contributions:

  • It introduces CALMQA, a multilingual long-form QA dataset with 2.6K culturally specific or culturally agnostic questions covering 23 languages, ranging from high- to low-resource languages .
  • The paper evaluates seven state-of-the-art models on CALMQA using the new metric CALMSCORE, highlighting surface-level issues in model-generated answers to low-resource language questions, particularly from LLAMA-3-70B and MIXTRAL-8X22B. GEMINI-1.5-PRO faced challenges processing input in low-resource languages .
  • Human evaluation on CLAUDE-3-OPUS, GPT-4-TURBO, and MIXTRAL-8X22B with a subset of CALMQA revealed that while CLAUDE-3-OPUS and GPT-4-TURBO performed well on culturally agnostic questions, CLAUDE-3-OPUS's performance degraded significantly on culturally specific questions .

What work can be continued in depth?

Work that can be continued in depth typically involves projects or tasks that require further analysis, research, or development. This could include:

  1. Research projects that require more data collection, analysis, and interpretation.
  2. Complex problem-solving tasks that need further exploration and experimentation.
  3. Creative projects that can be expanded upon with more ideas and iterations.
  4. Skill development activities that require continuous practice and improvement.
  5. Long-term projects that need ongoing monitoring and adjustments.

If you have a specific type of work in mind, feel free to provide more details so I can give you a more tailored response.

Tables

2

Introduction
Background
Scarcity of Non-English Resources
The lack of multilingual QA datasets in various languages
Impact on Low-Resource Languages
Challenges faced by LLMs in Tswana, Tongan, and Afar
Objective
Introducing CaLMQA Dataset
Multilingual QA resource in 23 languages
CALM-Score Metric
Development and evaluation of model performance
Method
Data Collection
Dataset Creation
Multilingual long-form question-answer pairs
Language Coverage
Inclusion of 23 diverse languages
Data Preprocessing
Data Cleaning
Removing noise and inconsistencies
Annotation Process
Human assessments for factual accuracy and cultural nuances
Model Analysis
AI Models Evaluated
CLAUDE-3-OPUS
Performance in culturally specific questions
GPT-4-TURBO
Factuality issues, illogical and irrelevant responses
MIXTRAL-8X22B
Hallucinations, cultural missteps, and limitations
Results and Findings
Model Performance Analysis
Factuality Comparison
GPT-4-TURBO's shortcomings
Cultural Awareness
MIXTRAL-8X22B's cultural missteps
Strengths and Weaknesses
CLAUDE-3-OPUS as a standout
Implications and Future Directions
Cultural Sensitivity in AI
The need for culturally aware systems
Research Priorities
Multilingual LLMs and QA improvements
Directions for Developers
Recommendations for enhancing model performance across languages
Conclusion
The CaLMQA Dataset's Significance
Addressing language gaps in QA research
Call to Action
Encouragement for further research and development in multilingual AI.
Basic info
papers
computation and language
machine learning
artificial intelligence
Advanced features
Insights
What is the primary focus of the CaLMQA dataset?
How do large language models perform on low-resource languages according to the study?
In which languages does the CaLMQA dataset provide a long-form QA resource?
What metric is introduced in the study to evaluate the performance of models on the CaLMQA dataset?

CaLMQA: Exploring culturally specific long-form question answering across 23 languages

Shane Arora, Marzena Karpinska, Hung-Ting Chen, Ipsita Bhattacharjee, Mohit Iyyer, Eunsol Choi·June 25, 2024

Summary

The CaLMQA dataset, a multilingual long-form QA resource in 23 languages, addresses the scarcity of non-English research. It reveals that large language models struggle with low-resource languages and culturally nuanced questions, particularly affecting Tswana, Tongan, and Afar. The study introduces the CALM-Score metric to evaluate performance. Human assessments show that models often lack factual accuracy, omit important details, and exhibit language inconsistencies, emphasizing the need for more research on multilingual LLMs and culturally diverse QA. The table comparing three AI models (CLAUDE-3-OPUS, GPT-4-TURBO, and MIXTRAL-8X22B) reveals factuality issues, with GPT-4-TURBO having more illogical and irrelevant responses, MIXTRAL-8X22B showing hallucinations and cultural missteps, and CLAUDE-3-OPUS excelling in culturally specific questions. Overall, the paper underscores the importance of cultural awareness in AI systems and the need for improved performance across languages.
Mind map
CLAUDE-3-OPUS as a standout
Strengths and Weaknesses
MIXTRAL-8X22B's cultural missteps
Cultural Awareness
GPT-4-TURBO's shortcomings
Factuality Comparison
Hallucinations, cultural missteps, and limitations
MIXTRAL-8X22B
Factuality issues, illogical and irrelevant responses
GPT-4-TURBO
Performance in culturally specific questions
CLAUDE-3-OPUS
Human assessments for factual accuracy and cultural nuances
Annotation Process
Removing noise and inconsistencies
Data Cleaning
Inclusion of 23 diverse languages
Language Coverage
Multilingual long-form question-answer pairs
Dataset Creation
Development and evaluation of model performance
CALM-Score Metric
Multilingual QA resource in 23 languages
Introducing CaLMQA Dataset
Challenges faced by LLMs in Tswana, Tongan, and Afar
Impact on Low-Resource Languages
The lack of multilingual QA datasets in various languages
Scarcity of Non-English Resources
Encouragement for further research and development in multilingual AI.
Call to Action
Addressing language gaps in QA research
The CaLMQA Dataset's Significance
Recommendations for enhancing model performance across languages
Directions for Developers
Multilingual LLMs and QA improvements
Research Priorities
The need for culturally aware systems
Cultural Sensitivity in AI
Model Performance Analysis
AI Models Evaluated
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Implications and Future Directions
Results and Findings
Model Analysis
Method
Introduction
Outline
Introduction
Background
Scarcity of Non-English Resources
The lack of multilingual QA datasets in various languages
Impact on Low-Resource Languages
Challenges faced by LLMs in Tswana, Tongan, and Afar
Objective
Introducing CaLMQA Dataset
Multilingual QA resource in 23 languages
CALM-Score Metric
Development and evaluation of model performance
Method
Data Collection
Dataset Creation
Multilingual long-form question-answer pairs
Language Coverage
Inclusion of 23 diverse languages
Data Preprocessing
Data Cleaning
Removing noise and inconsistencies
Annotation Process
Human assessments for factual accuracy and cultural nuances
Model Analysis
AI Models Evaluated
CLAUDE-3-OPUS
Performance in culturally specific questions
GPT-4-TURBO
Factuality issues, illogical and irrelevant responses
MIXTRAL-8X22B
Hallucinations, cultural missteps, and limitations
Results and Findings
Model Performance Analysis
Factuality Comparison
GPT-4-TURBO's shortcomings
Cultural Awareness
MIXTRAL-8X22B's cultural missteps
Strengths and Weaknesses
CLAUDE-3-OPUS as a standout
Implications and Future Directions
Cultural Sensitivity in AI
The need for culturally aware systems
Research Priorities
Multilingual LLMs and QA improvements
Directions for Developers
Recommendations for enhancing model performance across languages
Conclusion
The CaLMQA Dataset's Significance
Addressing language gaps in QA research
Call to Action
Encouragement for further research and development in multilingual AI.
Key findings
7

Paper digest

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

The paper attempts to address the challenge of culturally specific long-form question answering (LFQA) across 23 languages . This problem involves generating well-written, factual, and complete answers in various languages, which can be particularly challenging for low-resource languages due to the need for manual question writing and translation . The study focuses on the limitations and difficulties faced in producing high-quality answers in non-English languages, highlighting the struggles models encounter in generating accurate and fluent responses .

While the issue of multilingual LFQA is not entirely new, the paper sheds light on the complexities and limitations associated with generating culturally specific answers across a wide range of languages . The research emphasizes the need for comprehensive metrics to evaluate the overall quality of answers in multilingual LFQA, indicating that current language models still face challenges in producing accurate and fluent responses in languages other than English .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis that there is a significant increase in surface-level issues in model-generated answers to low-resource language questions, particularly those generated by LLAMA-3-70B and MIXTRAL-8X22B, as revealed through the evaluation on CALMQA using the new metric CALMSCORE . Additionally, the study highlights the necessity for more robust automatic evaluation metrics effective across multiple languages and emphasizes the need for enhanced multilingual instruction tuning to address the prevalence of responses primarily generated in English by certain models .


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

The paper "CaLMQA: Exploring culturally specific long-form question answering across 23 languages" introduces several new ideas, methods, and models . Some of the key contributions include:

  • Medexpqa: A multilingual benchmark for large language models in medical question answering .
  • Model Family: The Claude 3 Model Family consisting of Opus, Sonnet, and Haiku models .
  • GPT-4 Technical Report: Details about the GPT-4 model, its capabilities, and technical specifications .
  • Model Release Blog: Information about the GPT-4o model release by OpenAI .
  • Aya Model: An instruction finetuned open-access multilingual language model .
  • Llama 3 Model Card: Details about the Llama 3 model .
  • Cheaper, Better, Faster, Stronger: A technical report by Mistral AI .
  • GeoMLAMA: Geo-diverse commonsense probing on multilingual pre-trained language models .
  • PhotoshopQuiA: A corpus of non-factoid questions and answers for why-question answering .
  • MLQA: Evaluation of cross-lingual extractive question answering .
  • Mkqa: A linguistically diverse benchmark for multilingual open domain question answering .
  • Tydi QA: A benchmark for information-seeking question answering in typologically diverse languages .
  • Factscore: Fine-grained atomic evaluation of factual precision in long-form text generation .
  • Comparing Hallucination Detection Metrics: Evaluation of metrics for multilingual generation .
  • Hurdles to Progress in Long-form Question Answering: Challenges and obstacles in advancing long-form question answering .
  • Critical Evaluation of Evaluations for Long-form Question Answering: Assessment of evaluation methods for long-form question answering . The paper "CaLMQA: Exploring culturally specific long-form question answering across 23 languages" introduces several characteristics and advantages compared to previous methods. Here are some key points based on the details in the paper:
  1. Culturally Specific Question Answering: The paper focuses on culturally specific long-form question answering across 23 languages, which is a significant advancement compared to previous methods that may have been limited to specific languages or regions.

  2. Multilingual Benchmark: The introduction of the Medexpqa benchmark for large language models in medical question answering provides a standardized evaluation platform for assessing the performance of models across different languages and medical domains.

  3. Model Family: The Claude 3 Model Family, including Opus, Sonnet, and Haiku models, offers a range of models with varying capabilities and architectures, allowing for more flexibility and customization based on specific task requirements.

  4. GPT-4 Technical Report: The detailed technical report on the GPT-4 model provides insights into its architecture, training process, and performance metrics, enabling researchers and practitioners to better understand and utilize the model for various applications.

  5. Model Release Information: The paper includes information about the release of the GPT-4o model by OpenAI, highlighting the continuous development and deployment of state-of-the-art language models for the research community.

  6. Instruction Finetuned Model: The Aya model, which is instruction finetuned and open-access, offers a unique approach to training language models that can potentially improve performance on specific tasks or domains.

  7. Model Card: The Llama 3 Model Card provides essential details about the model's capabilities, limitations, and potential biases, promoting transparency and accountability in AI model development and deployment.

  8. Diverse Evaluation Benchmarks: The paper introduces various evaluation benchmarks such as MLQA, Mkqa, Tydi QA, and GeoMLAMA, which cover a wide range of languages and question types, enabling comprehensive assessment of model performance across different linguistic and cultural contexts.

Overall, the characteristics and advantages presented in the paper demonstrate a significant advancement in the field of long-form question answering, particularly in terms of cultural diversity, model transparency, and evaluation benchmarking compared to previous methods.


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 culturally specific long-form question answering, there are several related research papers and notable researchers mentioned in the provided context . Some of the noteworthy researchers in this field include Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, and many others .

One of the key solutions mentioned in the research papers is the development of benchmarks and datasets for culturally specific question answering, such as the "WikiHowQA" benchmark . These benchmarks aim to provide a comprehensive evaluation platform for multi-document non-factoid question answering, contributing to the advancement of culturally aware language technologies .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate seven state-of-the-art models on the CALMQA dataset using the new metric CALMSCORE. These experiments aimed to assess the performance of the models on 2.6K culturally specific or culturally agnostic questions covering 23 languages ranging from high- to low-resource settings. The results revealed a significant increase in surface-level issues in model-generated answers, particularly for low-resource language questions, with LLAMA-3-70B and MIXTRAL-8X22B showing challenges in processing such inputs. GEMINI-1.5-PRO encountered API errors when handling low-resource languages. Human evaluation was conducted on CLAUDE-3-OPUS, GPT-4-TURBO, and MIXTRAL-8X22B using a subset of CALMQA, showing that while CLAUDE-3-OPUS and GPT-4-TURBO performed well on culturally agnostic questions, CLAUDE-3-OPUS's performance degraded significantly on culturally specific questions .


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

The dataset used for quantitative evaluation in the study is CALMQA . The code for the data labeling software used in the project, Label Studio, is open source and available on GitHub .


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 in the paper provide valuable insights to support the scientific hypotheses that need to be verified. The study evaluates various aspects of question answering systems across different languages, highlighting both positive and negative feedback from annotators in terms of artificiality, fluency, and clarity of the answers provided . The results of the experiments, such as the analysis of different models and their impact on the responses, contribute to understanding the effectiveness and naturalness of the question answering process . The feedback from annotators regarding the structure of the answers, including the use of fact enumerations, sheds light on how these elements can influence the perceived human-likeness of the responses . Overall, the experiments offer a comprehensive evaluation of question answering systems in multiple languages, providing valuable insights for verifying scientific hypotheses related to the effectiveness and naturalness of such systems.


What are the contributions of this paper?

The paper "CaLMQA: Exploring culturally specific long-form question answering across 23 languages" makes several contributions:

  • It introduces CALMQA, a multilingual long-form QA dataset with 2.6K culturally specific or culturally agnostic questions covering 23 languages, ranging from high- to low-resource languages .
  • The paper evaluates seven state-of-the-art models on CALMQA using the new metric CALMSCORE, highlighting surface-level issues in model-generated answers to low-resource language questions, particularly from LLAMA-3-70B and MIXTRAL-8X22B. GEMINI-1.5-PRO faced challenges processing input in low-resource languages .
  • Human evaluation on CLAUDE-3-OPUS, GPT-4-TURBO, and MIXTRAL-8X22B with a subset of CALMQA revealed that while CLAUDE-3-OPUS and GPT-4-TURBO performed well on culturally agnostic questions, CLAUDE-3-OPUS's performance degraded significantly on culturally specific questions .

What work can be continued in depth?

Work that can be continued in depth typically involves projects or tasks that require further analysis, research, or development. This could include:

  1. Research projects that require more data collection, analysis, and interpretation.
  2. Complex problem-solving tasks that need further exploration and experimentation.
  3. Creative projects that can be expanded upon with more ideas and iterations.
  4. Skill development activities that require continuous practice and improvement.
  5. Long-term projects that need ongoing monitoring and adjustments.

If you have a specific type of work in mind, feel free to provide more details so I can give you a more tailored response.

Tables
2
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