TIBSTC-CoT：一种用于语言模型链式推理的多领域指令数据集
TIBSTC-CoT: A Multi-Domain Instruction Dataset for Chain-of-Thought Reasoning in Language Models
AI总结：
TIBSTC-CoT通过链式推理提示法构建多领域藏语数据集，开发出具备推理能力的藏语LLM，提升低资源语言处理性能。
摘要：
为了解决藏语数据稀缺问题，一种由超过六百万人使用的低资源语言，我们介绍了TIBSTC-CoT，通过链式推理提示法利用大语言模型（LLMs）自动构建的大规模、多领域的藏语数据集。TIBSTC-CoT建立了在低资源环境下可扩展和可重复的数据库创建框架，涵盖多样化的领域和推理模式，对于语言理解和生成至关重要。基于此数据集，我们开发了Sunshine-thinking LLM系列，一系列以藏语为中心的LLMs，具备链式推理能力。完全在TIBSTC-CoT上训练的Sunshine-thinking在推理和生成性能上表现出色，与最先进的多语言LLMs相当。我们的工作标志着迈向包容性AI的重要一步，通过资源创建和模型创新实现了高质量的藏语语言处理。所有数据均可获取：https://github.com/Vicentvankor/sun-shine。
英文摘要：
To address the severe data scarcity in Tibetan, a low-resource language spoken by over six million people, we introduce TIBSTC-CoT, the large-scale, multi-domain Tibetan dataset automatically constructed via chain-of-thought prompting with large language models (LLMs). TIBSTC-CoT establishes a scalable and reproducible framework for dataset creation in low-resource settings, covering diverse domains and reasoning patterns essential for language understanding and generation. Building on this dataset, we develop the Sunshine-thinking LLM family, a series of Tibetan-centric LLMs equipped with chain-of-thought capabilities. Trained entirely on TIBSTC-CoT, Sunshine-thinking has demonstrated strong reasoning and generation performance, comparable to state-of-the-art (SOTA) multilingual LLMs. Our work marks a significant step toward inclusive AI by enabling high-quality Tibetan language processing through both resource creation and model innovation. All data are available: https://github.com/Vicentvankor/sun-shine.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
