Adapting Language-Specific LLMs to a Reasoning Model in One Day via Model Merging - An Open Recipe

• URL: http://arxiv.org/abs/2502.09056v2
• Date: Mon, 17 Feb 2025 13:16:00 GMT
• Title: Adapting Language-Specific LLMs to a Reasoning Model in One Day via Model Merging - An Open Recipe
• Authors: Kunat Pipatanakul, Pittawat Taveekitworachai, Potsawee Manakul, Kasima Tharnpipitchai,
• Abstract summary: This paper aims to enhance the reasoning capabilities of language-specific large language models (LLMs) DeepSeek R1 excels in reasoning but primarily benefits high-resource languages such as English and Chinese. Low-resource languages remain underserved due to the dominance of English-centric training data and model optimizations.
• Score: 12.076338505539194
• License:
• Abstract: This paper investigates data selection and model merging methodologies aimed at incorporating advanced reasoning capabilities such as those of DeepSeek R1 into language-specific large language models (LLMs), with a particular focus on the Thai LLM. Our goal is to enhance the reasoning capabilities of language-specific LLMs while maintaining their target language abilities. DeepSeek R1 excels in reasoning but primarily benefits high-resource languages such as English and Chinese. However, low-resource languages remain underserved due to the dominance of English-centric training data and model optimizations, which limit performance in these languages. This limitation results in unreliable code-switching and diminished effectiveness on tasks in low-resource languages. Meanwhile, local and regional LLM initiatives have attempted to bridge this gap by developing language-specific LLMs that focus on improving local linguistic fidelity. We demonstrate that, with only publicly available datasets and a computational budget of $120, it is possible to enhance the reasoning capabilities of language-specific LLMs to match the level of DeepSeek R1, without compromising their performance on target language tasks.

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