MindMerger: Efficient Boosting LLM Reasoning in non-English Languages

Zixian Huang, Wenhao Zhu, Gong Cheng, Lei Li, Fei Yuan·May 27, 2024

Summary

The paper "MindMerger: Efficient Boosting of LLM Reasoning in Non-English Languages" addresses the disparity in reasoning capabilities between English and non-English large language models (LLMs). It proposes a two-step method that combines LLMs with external language understanding from multilingual models to enhance reasoning performance, particularly in low-resource languages. The approach involves mapping external language understanding into LLMs and training them collaboratively through query translation tasks. Experiments on multilingual reasoning and language understanding datasets show that MindMerger outperforms existing methods, with substantial improvements in low-resource settings without updating LLM parameters. The study highlights the effectiveness of augmentation-based strategies, and MindMerger-Soft and MindMerger-Hard variants are found to be the most effective, demonstrating the potential to bridge the gap in multilingual reasoning capabilities.

Paper digest

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

The paper "MindMerger: Efficient Boosting LLM Reasoning in non-English Languages" aims to address the gap in reasoning capabilities between English and non-English languages within Large Language Models (LLMs) . Specifically, it focuses on enhancing multilingual reasoning performance by merging LLMs with external language understanding capabilities from pre-trained multilingual models . This problem of improving multilingual reasoning in LLMs is not entirely new, as previous works have attempted to fine-tune LLMs for reasoning in non-English languages or replace non-English inputs with English translations . However, the proposed method in the paper, MindMerger, introduces a novel approach to boost multilingual reasoning by leveraging both external and built-in language capabilities of LLMs through a two-step training scheme .


What scientific hypothesis does this paper seek to validate?

The paper "MindMerger: Efficient Boosting LLM Reasoning in non-English Languages" aims to validate the scientific hypothesis related to boosting LLM reasoning in non-English languages . The research focuses on enhancing the efficiency of Large Language Models (LLMs) for reasoning tasks specifically in languages other than English . The study likely explores methods to improve the performance and effectiveness of LLMs in non-English language contexts, contributing to advancements in natural language processing and multilingual reasoning capabilities .


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

The paper "MindMerger: Efficient Boosting LLM Reasoning in non-English Languages" proposes innovative methods and models for enhancing large language models (LLMs) in non-English languages. Here are some key ideas, methods, and models discussed in the paper:

  1. MindMerger Variants:

    • The paper introduces two variants of MindMerger: MindMerger-Soft and MindMerger-Hard. MindMerger-Soft augments LLM prompts with translated queries, while MindMerger-Hard replaces LLM prompts with English translations .
  2. Comparison with Baselines:

    • The study compares the proposed methods with various baselines, including MonoReason, MultiReason-SFT, MultiReason-Lora, QAlign, Translate-En, and LangBridge. These baselines utilize different approaches such as fine-tuning, query translation, and replacement-based methods .
  3. Multilingual Models:

    • The paper utilizes multilingual models like mT5-xl for encoding, NLLB200-3.3B for translation, and Llama 2-7B for LLM across all methods. It also explores different multilingual model architectures like encoder-only, decoder-only, and encoder-decoder structures .
  4. Performance Evaluation:

    • Performance evaluation of the proposed methods is conducted against baselines using metrics like accuracy, precision, and recall. Results show that MindMerger-Soft and MindMerger-Hard outperform several baselines in various languages and reasoning tasks .
  5. Influence of Multilingual Models:

    • The study analyzes the impact of different multilingual models on the proposed methods. It investigates how encoder-only, decoder-only, and encoder-decoder architectures affect the performance of MindMerger and baselines .
  6. Cross-Lingual Reasoning:

    • The paper contributes to the field of cross-lingual reasoning by enhancing LLM capabilities in non-English languages. It addresses the challenges of multilingual reasoning and proposes effective strategies for improving performance in diverse linguistic contexts .

Overall, the paper introduces novel approaches like MindMerger-Soft and MindMerger-Hard, compares them with existing baselines, leverages multilingual models, evaluates performance metrics, and advances cross-lingual reasoning capabilities in large language models . The paper "MindMerger: Efficient Boosting LLM Reasoning in non-English Languages" introduces innovative methods, MindMerger-Soft and MindMerger-Hard, which offer distinct characteristics and advantages compared to previous methods . Here are the key points based on the details in the paper:

  1. Characteristics of MindMerger:

    • MindMerger-Soft: This method augments LLM prompts with translated queries, enhancing the reasoning capabilities of LLMs in non-English languages .
    • MindMerger-Hard: In contrast, MindMerger-Hard replaces LLM prompts with translated queries, leveraging external language understanding capabilities from multilingual models .
  2. Advantages Over Baselines:

    • Improved Performance: MindMerger-Soft and MindMerger-Hard outperform various baselines, including MonoReason, MultiReason-SFT, MultiReason-Lora, QAlign, Translate-En, and LangBridge, across different languages and reasoning tasks .
    • Enhanced Multilingual Reasoning: MindMerger demonstrates superior performance in multilingual reasoning datasets, achieving significant accuracy improvements over baselines, especially in low-resource languages .
    • Sequential Training Scheme: The two-stage training scheme of MindMerger helps embed external capabilities and effectively utilize internal and external capabilities, leading to enhanced reasoning and language understanding in LLMs .
  3. Influence of Multilingual Models:

    • The paper analyzes the impact of different multilingual models, including encoder-only, decoder-only, and encoder-decoder architectures, on the performance of MindMerger and baselines .
  4. Augmentation Strategy:

    • The augmentation strategy employed by MindMerger-Soft further enhances reasoning capabilities, especially in low-resource languages, showcasing improvements in accuracy based on different multilingual models .

In summary, the characteristics of MindMerger methods, their advantages over baselines, the influence of multilingual models, and the effectiveness of the augmentation strategy highlight the significant contributions of the proposed approaches in boosting LLM reasoning in non-English 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 efficient boosting in large language models (LLMs) reasoning in non-English languages, several related research papers and notable researchers have contributed to advancements in this area . Noteworthy researchers in this field include Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed, Shima Imani, Liang Du, Harsh Shrivastava, Weisen Jiang, Han Shi, Longhui Yu, Zhengying Liu, Yu Zhang, Zhenguo Li, James T. Kwok, Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and many others.

The key to the solution mentioned in the paper "MindMerger: Efficient Boosting LLM Reasoning in non-English Languages" lies in the development of methodologies and techniques for enhancing reasoning capabilities in large language models, particularly focusing on non-English languages. These advancements aim to improve mathematical reasoning, verification, and composition abilities of LLMs, thereby expanding their applications and effectiveness in various domains .


How were the experiments in the paper designed?

The experiments in the paper were designed with the following structure:

  • Supplementary Experiments: Included various aspects such as training set size in the augmentation stage, the selection of mapping layers, the usage of encoder-decoder model, quantitative analysis on representation space changes, and translation performance .
  • Complete Results: This section involved comparing the proposed methods, MindMerger-Soft and MindMerger-Hard, with different categories of baselines, including MonoReason, MultiReason-SFT, MultiReason-Lora, and QAlign. It also compared replacement-based methods like Translate-En and LangBridge .
  • Limitations: The limitations of the experiments were likely discussed in this section, although specific details were not provided in the context .
  • Other Tables: This section included various tables such as dataset statistics, prompt templates, and examples of training data .

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

The dataset used for quantitative evaluation in the study is the multilingual mathematical reasoning dataset MGSM and the MSVAMP dataset . The code used in the study is not explicitly mentioned to be open source in the provided context.


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 substantial support for the scientific hypotheses that require verification. The study includes a detailed outline of supplementary experiments, complete results, limitations, and other relevant tables that contribute to the thorough analysis of the hypotheses . The experimental data, such as the accuracy across different languages and models, as well as the translation performance, offer a comprehensive evaluation of the scientific hypotheses . Additionally, the references to previous studies and datasets further strengthen the credibility and validity of the findings, enhancing the support for the scientific hypotheses .


What are the contributions of this paper?

The paper "MindMerger: Efficient Boosting LLM Reasoning in non-English Languages" makes several contributions:

  • It introduces LLM augmented llms to expand capabilities through composition .
  • It presents Langbridge, a system for multilingual reasoning without multilingual supervision .
  • The paper discusses Xnli, which evaluates cross-lingual sentence representations .
  • It covers the work on unsupervised cross-lingual representation learning at scale .
  • The paper addresses the development of Lego-mt for massively multilingual machine translation .
  • It explores the concept of LoRA for low-rank adaptation of large language models .
  • The paper discusses the development of MetaMath for creating mathematical questions for large language models .
  • It presents Mistral 7b, a system for forward-backward reasoning in large language models for verification .
  • The paper introduces Mathprompter for mathematical reasoning using large language models .
  • It discusses the development of Translate-En for multiple-choice question answering .

What work can be continued in depth?

To delve deeper into the field of large language models (LLMs) and multilingual reasoning, further research can be conducted on strategies to enhance reasoning capabilities in non-English languages, especially those with limited linguistic resources. One avenue for exploration is the utilization of external models to address the shortcomings of LLMs in multilingual reasoning. This includes investigating relearning-based strategies that leverage translation models to create multilingual training data for fine-tuning LLMs to improve reasoning across different languages . Additionally, the replacement-based strategy, which involves using translation models to translate non-English queries into English text for substituting the non-English input, presents another area for in-depth investigation . These approaches aim to bridge the performance gap between English and non-English reasoning by leveraging external models and innovative strategies to enhance multilingual reasoning capabilities.


Introduction
Background
Disparity in LLM reasoning capabilities between English and non-English languages
Importance of multilingual reasoning in a global context
Objective
To enhance LLM reasoning performance in low-resource languages
Develop a method that does not require updating LLM parameters
Method
Data Collection
Selection of multilingual reasoning and language understanding datasets
Datasets with diverse language coverage and varying resource levels
Data Preprocessing
Mapping external language understanding to LLM input format
Query translation tasks for cross-lingual compatibility
MindMerger Approach
MindMerger-Base
Combining LLMs with external language models
Translation of input queries for joint understanding
MindMerger-Soft
Soft parameter sharing between LLMs and external models
Balancing performance and computational efficiency
MindMerger-Hard
Hard parameter transfer and fine-tuning on translated tasks
Higher reasoning improvement but more resource-intensive
Training Strategy
Collaborative learning through query translation tasks
Evaluation of different training methods
Experiments and Results
Performance Evaluation
Comparison with existing multilingual reasoning methods
Metrics: accuracy, F1 score, and reasoning gap reduction
Low-Resource Settings
Improved performance in languages with limited data
Real-world scenarios and case studies
Discussion
Augmentation-based strategies for enhancing LLMs
Limitations and potential for future improvements
Ethical considerations in multilingual model development
Conclusion
Summary of key findings and contributions
Implications for future research and practical applications
Potential to bridge the reasoning gap in non-English languages
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
What is the primary focus of the paper "MindMerger"?
How do MindMerger-Soft and MindMerger-Hard variants compare in terms of improving multilingual reasoning performance?
How does the proposed method in the paper address the disparity between English and non-English LLMs?
What are the two-step methods employed by MindMerger to enhance reasoning in non-English languages?

MindMerger: Efficient Boosting LLM Reasoning in non-English Languages

Zixian Huang, Wenhao Zhu, Gong Cheng, Lei Li, Fei Yuan·May 27, 2024

Summary

The paper "MindMerger: Efficient Boosting of LLM Reasoning in Non-English Languages" addresses the disparity in reasoning capabilities between English and non-English large language models (LLMs). It proposes a two-step method that combines LLMs with external language understanding from multilingual models to enhance reasoning performance, particularly in low-resource languages. The approach involves mapping external language understanding into LLMs and training them collaboratively through query translation tasks. Experiments on multilingual reasoning and language understanding datasets show that MindMerger outperforms existing methods, with substantial improvements in low-resource settings without updating LLM parameters. The study highlights the effectiveness of augmentation-based strategies, and MindMerger-Soft and MindMerger-Hard variants are found to be the most effective, demonstrating the potential to bridge the gap in multilingual reasoning capabilities.
Mind map
Evaluation of different training methods
Collaborative learning through query translation tasks
Higher reasoning improvement but more resource-intensive
Hard parameter transfer and fine-tuning on translated tasks
Balancing performance and computational efficiency
Soft parameter sharing between LLMs and external models
Translation of input queries for joint understanding
Combining LLMs with external language models
Training Strategy
MindMerger-Hard
MindMerger-Soft
MindMerger-Base
Real-world scenarios and case studies
Improved performance in languages with limited data
Metrics: accuracy, F1 score, and reasoning gap reduction
Comparison with existing multilingual reasoning methods
MindMerger Approach
Datasets with diverse language coverage and varying resource levels
Selection of multilingual reasoning and language understanding datasets
Develop a method that does not require updating LLM parameters
To enhance LLM reasoning performance in low-resource languages
Importance of multilingual reasoning in a global context
Disparity in LLM reasoning capabilities between English and non-English languages
Potential to bridge the reasoning gap in non-English languages
Implications for future research and practical applications
Summary of key findings and contributions
Ethical considerations in multilingual model development
Limitations and potential for future improvements
Augmentation-based strategies for enhancing LLMs
Low-Resource Settings
Performance Evaluation
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Discussion
Experiments and Results
Method
Introduction
Outline
Introduction
Background
Disparity in LLM reasoning capabilities between English and non-English languages
Importance of multilingual reasoning in a global context
Objective
To enhance LLM reasoning performance in low-resource languages
Develop a method that does not require updating LLM parameters
Method
Data Collection
Selection of multilingual reasoning and language understanding datasets
Datasets with diverse language coverage and varying resource levels
Data Preprocessing
Mapping external language understanding to LLM input format
Query translation tasks for cross-lingual compatibility
MindMerger Approach
MindMerger-Base
Combining LLMs with external language models
Translation of input queries for joint understanding
MindMerger-Soft
Soft parameter sharing between LLMs and external models
Balancing performance and computational efficiency
MindMerger-Hard
Hard parameter transfer and fine-tuning on translated tasks
Higher reasoning improvement but more resource-intensive
Training Strategy
Collaborative learning through query translation tasks
Evaluation of different training methods
Experiments and Results
Performance Evaluation
Comparison with existing multilingual reasoning methods
Metrics: accuracy, F1 score, and reasoning gap reduction
Low-Resource Settings
Improved performance in languages with limited data
Real-world scenarios and case studies
Discussion
Augmentation-based strategies for enhancing LLMs
Limitations and potential for future improvements
Ethical considerations in multilingual model development
Conclusion
Summary of key findings and contributions
Implications for future research and practical applications
Potential to bridge the reasoning gap in non-English languages

Paper digest

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

The paper "MindMerger: Efficient Boosting LLM Reasoning in non-English Languages" aims to address the gap in reasoning capabilities between English and non-English languages within Large Language Models (LLMs) . Specifically, it focuses on enhancing multilingual reasoning performance by merging LLMs with external language understanding capabilities from pre-trained multilingual models . This problem of improving multilingual reasoning in LLMs is not entirely new, as previous works have attempted to fine-tune LLMs for reasoning in non-English languages or replace non-English inputs with English translations . However, the proposed method in the paper, MindMerger, introduces a novel approach to boost multilingual reasoning by leveraging both external and built-in language capabilities of LLMs through a two-step training scheme .


What scientific hypothesis does this paper seek to validate?

The paper "MindMerger: Efficient Boosting LLM Reasoning in non-English Languages" aims to validate the scientific hypothesis related to boosting LLM reasoning in non-English languages . The research focuses on enhancing the efficiency of Large Language Models (LLMs) for reasoning tasks specifically in languages other than English . The study likely explores methods to improve the performance and effectiveness of LLMs in non-English language contexts, contributing to advancements in natural language processing and multilingual reasoning capabilities .


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

The paper "MindMerger: Efficient Boosting LLM Reasoning in non-English Languages" proposes innovative methods and models for enhancing large language models (LLMs) in non-English languages. Here are some key ideas, methods, and models discussed in the paper:

  1. MindMerger Variants:

    • The paper introduces two variants of MindMerger: MindMerger-Soft and MindMerger-Hard. MindMerger-Soft augments LLM prompts with translated queries, while MindMerger-Hard replaces LLM prompts with English translations .
  2. Comparison with Baselines:

    • The study compares the proposed methods with various baselines, including MonoReason, MultiReason-SFT, MultiReason-Lora, QAlign, Translate-En, and LangBridge. These baselines utilize different approaches such as fine-tuning, query translation, and replacement-based methods .
  3. Multilingual Models:

    • The paper utilizes multilingual models like mT5-xl for encoding, NLLB200-3.3B for translation, and Llama 2-7B for LLM across all methods. It also explores different multilingual model architectures like encoder-only, decoder-only, and encoder-decoder structures .
  4. Performance Evaluation:

    • Performance evaluation of the proposed methods is conducted against baselines using metrics like accuracy, precision, and recall. Results show that MindMerger-Soft and MindMerger-Hard outperform several baselines in various languages and reasoning tasks .
  5. Influence of Multilingual Models:

    • The study analyzes the impact of different multilingual models on the proposed methods. It investigates how encoder-only, decoder-only, and encoder-decoder architectures affect the performance of MindMerger and baselines .
  6. Cross-Lingual Reasoning:

    • The paper contributes to the field of cross-lingual reasoning by enhancing LLM capabilities in non-English languages. It addresses the challenges of multilingual reasoning and proposes effective strategies for improving performance in diverse linguistic contexts .

Overall, the paper introduces novel approaches like MindMerger-Soft and MindMerger-Hard, compares them with existing baselines, leverages multilingual models, evaluates performance metrics, and advances cross-lingual reasoning capabilities in large language models . The paper "MindMerger: Efficient Boosting LLM Reasoning in non-English Languages" introduces innovative methods, MindMerger-Soft and MindMerger-Hard, which offer distinct characteristics and advantages compared to previous methods . Here are the key points based on the details in the paper:

  1. Characteristics of MindMerger:

    • MindMerger-Soft: This method augments LLM prompts with translated queries, enhancing the reasoning capabilities of LLMs in non-English languages .
    • MindMerger-Hard: In contrast, MindMerger-Hard replaces LLM prompts with translated queries, leveraging external language understanding capabilities from multilingual models .
  2. Advantages Over Baselines:

    • Improved Performance: MindMerger-Soft and MindMerger-Hard outperform various baselines, including MonoReason, MultiReason-SFT, MultiReason-Lora, QAlign, Translate-En, and LangBridge, across different languages and reasoning tasks .
    • Enhanced Multilingual Reasoning: MindMerger demonstrates superior performance in multilingual reasoning datasets, achieving significant accuracy improvements over baselines, especially in low-resource languages .
    • Sequential Training Scheme: The two-stage training scheme of MindMerger helps embed external capabilities and effectively utilize internal and external capabilities, leading to enhanced reasoning and language understanding in LLMs .
  3. Influence of Multilingual Models:

    • The paper analyzes the impact of different multilingual models, including encoder-only, decoder-only, and encoder-decoder architectures, on the performance of MindMerger and baselines .
  4. Augmentation Strategy:

    • The augmentation strategy employed by MindMerger-Soft further enhances reasoning capabilities, especially in low-resource languages, showcasing improvements in accuracy based on different multilingual models .

In summary, the characteristics of MindMerger methods, their advantages over baselines, the influence of multilingual models, and the effectiveness of the augmentation strategy highlight the significant contributions of the proposed approaches in boosting LLM reasoning in non-English 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 efficient boosting in large language models (LLMs) reasoning in non-English languages, several related research papers and notable researchers have contributed to advancements in this area . Noteworthy researchers in this field include Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed, Shima Imani, Liang Du, Harsh Shrivastava, Weisen Jiang, Han Shi, Longhui Yu, Zhengying Liu, Yu Zhang, Zhenguo Li, James T. Kwok, Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and many others.

The key to the solution mentioned in the paper "MindMerger: Efficient Boosting LLM Reasoning in non-English Languages" lies in the development of methodologies and techniques for enhancing reasoning capabilities in large language models, particularly focusing on non-English languages. These advancements aim to improve mathematical reasoning, verification, and composition abilities of LLMs, thereby expanding their applications and effectiveness in various domains .


How were the experiments in the paper designed?

The experiments in the paper were designed with the following structure:

  • Supplementary Experiments: Included various aspects such as training set size in the augmentation stage, the selection of mapping layers, the usage of encoder-decoder model, quantitative analysis on representation space changes, and translation performance .
  • Complete Results: This section involved comparing the proposed methods, MindMerger-Soft and MindMerger-Hard, with different categories of baselines, including MonoReason, MultiReason-SFT, MultiReason-Lora, and QAlign. It also compared replacement-based methods like Translate-En and LangBridge .
  • Limitations: The limitations of the experiments were likely discussed in this section, although specific details were not provided in the context .
  • Other Tables: This section included various tables such as dataset statistics, prompt templates, and examples of training data .

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

The dataset used for quantitative evaluation in the study is the multilingual mathematical reasoning dataset MGSM and the MSVAMP dataset . The code used in the study is not explicitly mentioned to be open source in the provided context.


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 substantial support for the scientific hypotheses that require verification. The study includes a detailed outline of supplementary experiments, complete results, limitations, and other relevant tables that contribute to the thorough analysis of the hypotheses . The experimental data, such as the accuracy across different languages and models, as well as the translation performance, offer a comprehensive evaluation of the scientific hypotheses . Additionally, the references to previous studies and datasets further strengthen the credibility and validity of the findings, enhancing the support for the scientific hypotheses .


What are the contributions of this paper?

The paper "MindMerger: Efficient Boosting LLM Reasoning in non-English Languages" makes several contributions:

  • It introduces LLM augmented llms to expand capabilities through composition .
  • It presents Langbridge, a system for multilingual reasoning without multilingual supervision .
  • The paper discusses Xnli, which evaluates cross-lingual sentence representations .
  • It covers the work on unsupervised cross-lingual representation learning at scale .
  • The paper addresses the development of Lego-mt for massively multilingual machine translation .
  • It explores the concept of LoRA for low-rank adaptation of large language models .
  • The paper discusses the development of MetaMath for creating mathematical questions for large language models .
  • It presents Mistral 7b, a system for forward-backward reasoning in large language models for verification .
  • The paper introduces Mathprompter for mathematical reasoning using large language models .
  • It discusses the development of Translate-En for multiple-choice question answering .

What work can be continued in depth?

To delve deeper into the field of large language models (LLMs) and multilingual reasoning, further research can be conducted on strategies to enhance reasoning capabilities in non-English languages, especially those with limited linguistic resources. One avenue for exploration is the utilization of external models to address the shortcomings of LLMs in multilingual reasoning. This includes investigating relearning-based strategies that leverage translation models to create multilingual training data for fine-tuning LLMs to improve reasoning across different languages . Additionally, the replacement-based strategy, which involves using translation models to translate non-English queries into English text for substituting the non-English input, presents another area for in-depth investigation . These approaches aim to bridge the performance gap between English and non-English reasoning by leveraging external models and innovative strategies to enhance multilingual reasoning capabilities.

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