title: An Open Recipe: Adapting Language-Specific LLMs to a Reasoning Model in
    One Day via Model Merging
summary: 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.
paper_authors._id: 67aea8d7926b659c7e959bbc
paper_authors.name: Kunat Pipatanakul
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-14T12:04:29.437Z
paper_authors._id: 62d192c2d50433c35eb1b48e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62d192c2d50433c35eb1b48e/VjmDu8GOIuLuQNBQdQLLS.png
paper_authors.fullname: Kunat Pipatanakul
paper_authors.type: user
paper_authors.user: kunato
paper_authors._id: 67aea8d7926b659c7e959bbd
paper_authors.name: Pittawat Taveekitworachai
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-14T08:01:21.838Z
paper_authors._id: 615313b0793ef66b3324da1f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/615313b0793ef66b3324da1f/VyJniD3dxbV5a2CMgVVQ2.jpeg
paper_authors.fullname: Pittawat Taveekitworachai
paper_authors.type: user
paper_authors.user: pittawat
paper_authors._id: 67aea8d7926b659c7e959bbe
paper_authors.name: Potsawee Manakul
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-14T12:04:35.434Z
paper_authors._id: 63f6a050b4c9a104f4b95755
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63f6a050b4c9a104f4b95755/eJQyJkenSz536j-EGcpkH.jpeg
paper_authors.fullname: Potsawee Manakul
paper_authors.type: user
paper_authors.user: potsawee
paper_authors._id: 67aea8d7926b659c7e959bbf
paper_authors.name: Kasima Tharnpipitchai
paper_title: An Open Recipe: Adapting Language-Specific LLMs to a Reasoning Model in
    One Day via Model Merging
paper_summary: 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.
paper_ai_summary: The paper enhances the reasoning capabilities of language-specific LLMs, particularly for low-resource languages like Thai, by merging data selection methods and integrating advanced reasoning models.
paper_ai_keywords: DeepSeek R1 language-specific large language models data selection model merging reasoning capabilities low-resource languages English-centric training data computational budget local linguistic fidelity target language tasks