MindMerger: Efficient Boosting LLM Reasoning in non-English Languages

• URL: http://arxiv.org/abs/2405.17386v1
• Date: Mon, 27 May 2024 17:41:54 GMT
• Title: MindMerger: Efficient Boosting LLM Reasoning in non-English Languages
• Authors: Zixian Huang, Wenhao Zhu, Gong Cheng, Lei Li, Fei Yuan,
• Abstract summary: Reasoning capabilities are crucial for Large Language Models (LLMs) We propose MindMerger, which merges LLMs with the external language understanding capabilities from multilingual models. MindMerger consistently outperforms all baselines, especially in low-resource languages.
• Score: 26.334092384176518
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reasoning capabilities are crucial for Large Language Models (LLMs), yet a notable gap exists between English and non-English languages. To bridge this disparity, some works fine-tune LLMs to relearn reasoning capabilities in non-English languages, while others replace non-English inputs with an external model's outputs such as English translation text to circumvent the challenge of LLM understanding non-English. Unfortunately, these methods often underutilize the built-in skilled reasoning and useful language understanding capabilities of LLMs. In order to better utilize the minds of reasoning and language understanding in LLMs, we propose a new method, namely MindMerger, which merges LLMs with the external language understanding capabilities from multilingual models to boost the multilingual reasoning performance. Furthermore, a two-step training scheme is introduced to first train to embeded the external capabilities into LLMs and then train the collaborative utilization of the external capabilities and the built-in capabilities in LLMs. Experiments on three multilingual reasoning datasets and a language understanding dataset demonstrate that MindMerger consistently outperforms all baselines, especially in low-resource languages. Without updating the parameters of LLMs, the average accuracy improved by 6.7% and 8.0% across all languages and low-resource languages on the MGSM dataset, respectively.

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