Typhoon T1: An Open Thai Reasoning Model
This work addresses the limited availability of reasoning models for low-resource languages like Thai, providing an open foundation for further research, though it is incremental as it applies existing reasoning model concepts to a new language context.
The paper tackles the challenge of developing a reasoning model for low-resource languages by introducing Typhoon T1, an open Thai reasoning model that uses supervised fine-tuning with open datasets, resulting in a cost-effective approach that generalizes across domains and generates reasoning traces in Thai.
This paper introduces Typhoon T1, an open effort to develop an open Thai reasoning model. A reasoning model is a relatively new type of generative model built on top of large language models (LLMs). A reasoning model generates a long chain of thought before arriving at a final answer, an approach found to improve performance on complex tasks. However, details on developing such a model are limited, especially for reasoning models that can generate traces in a low-resource language. Typhoon T1 presents an open effort that dives into the details of developing a reasoning model in a more cost-effective way by leveraging supervised fine-tuning using open datasets, instead of reinforcement learning. This paper shares the details about synthetic data generation and training, as well as our dataset and model weights. Additionally, we provide insights gained from developing a reasoning model that generalizes across domains and is capable of generating reasoning traces in a low-resource language, using Thai as an example. We hope this open effort provides a foundation for further research in this field.