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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.01977 |
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| _version_ | 1866908713905815552 |
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| author | Gao, Fan Huang, Cheng Tashi, Nyima Liu, Yutong Wang, Xiangxiang Tsering, Thupten Ma-bao, Ban Duojie, Renzeg Luosang, Gadeng Dongrub, Rinchen Tashi, Dorje Feng, Xiao Wang, Hao Yu, Yongbin |
| author_facet | Gao, Fan Huang, Cheng Tashi, Nyima Liu, Yutong Wang, Xiangxiang Tsering, Thupten Ma-bao, Ban Duojie, Renzeg Luosang, Gadeng Dongrub, Rinchen Tashi, Dorje Feng, Xiao Wang, Hao Yu, Yongbin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01977 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TIBSTC-CoT: A Multi-Domain Instruction Dataset for Chain-of-Thought Reasoning in Language Models Gao, Fan Huang, Cheng Tashi, Nyima Liu, Yutong Wang, Xiangxiang Tsering, Thupten Ma-bao, Ban Duojie, Renzeg Luosang, Gadeng Dongrub, Rinchen Tashi, Dorje Feng, Xiao Wang, Hao Yu, Yongbin Computation and Language Artificial Intelligence 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. |
| title | TIBSTC-CoT: A Multi-Domain Instruction Dataset for Chain-of-Thought Reasoning in Language Models |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2508.01977 |