用120美元预算，一天内让泰语大模型具备顶级推理能力
Adapting Language-Specific LLMs to a Reasoning Model in One Day via Model Merging -- An Open Recipe
- 通过模型融合与数据筛选，将深求R1推理能力迁移到泰语模型
- 仅用120美元计算成本，推理性能媲美DeepSeek R1
- 适合关注低资源语言模型优化的研究者与开发者
本文研究了数据选择与模型融合方法，旨在将DeepSeek R1等先进推理能力引入语言专用大语言模型（LLM），特别聚焦泰语模型。尽管DeepSeek R1在推理方面表现优异，但主要惠及英语和中文等高资源语言。由于训练数据和模型优化以英语为主，低资源语言的性能仍受限，导致代码切换不可靠、任务表现下降。为此，本地化大模型项目致力于提升本地语言表达的准确性。本研究证明，在仅使用公开数据集且计算成本控制在120美元的前提下，可使语言专用模型的推理能力达到DeepSeek R1水平，同时不损害其在目标语言任务上的表现。
原文摘要 · Abstract (English)
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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