Enhancing Reasoning Capabilities in Low-Resource Language Models
Overview of Large Language Models (LLMs)
Large Language Models (LLMs) have made great strides in complex reasoning tasks. However, there is a noticeable performance gap across different languages, especially for low-resource languages. Most training data focuses on English and Chinese, leaving other languages behind. Issues like incorrect character usage and code-switching complicate reasoning tasks.
Regional Initiatives for Low-Resource Languages
To tackle these challenges, various regional LLM projects have emerged. Initiatives like Typhoon, Sailor, EuroLLM, and others aim to adapt models for specific languages. However, the methods used to improve reasoning capabilities often lack transparency and require significant computational resources.
Innovative Research from Thailand
Researchers from SCB 10X R&D and SCBX Group in Bangkok have proposed a new method to enhance reasoning in Thai language models. Their approach combines data selection and model merging to achieve advanced reasoning capabilities similar to top models, all while using publicly available datasets and a modest budget of $1,201.
Methodology and Implementation
The research utilizes Typhoon2 70B Instruct and DeepSeek R1 70B Distill as base models. They apply Supervised Fine-Tuning (SFT) and merge the models to optimize performance. Key techniques include:
- Using LoRA for efficient training
- Employing advanced computational methods like FlashAttention-2
- Running training on powerful GPUs for optimal results
Results and Performance
The final model, Typhoon2-R1-70B, successfully combines reasoning capabilities with Thai language proficiency. It shows a 41.6% improvement over Typhoon2 and a 12.8% improvement over DeepSeek R1 in reasoning tasks.
Conclusion and Future Directions
This research highlights the potential of combining specialized models to enhance reasoning in low-resource languages. While there are limitations, such as the need for culturally aware reasoning, this work is a significant step forward.
Explore Further
For more details, check out the Paper. Follow us on Twitter and join our 75k+ ML SubReddit for updates.
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