TIB-STC: A Large-Scale Structured Tibetan Benchmark for Low-Resource Language Modeling

• URL: http://arxiv.org/abs/2503.18288v5
• Date: Mon, 04 Aug 2025 01:35:59 GMT
• Title: TIB-STC: A Large-Scale Structured Tibetan Benchmark for Low-Resource Language Modeling
• Authors: Cheng Huang, Fan Gao, Yutong Liu, Nyima Tashi, Xiangxiang Wang, Thupten Tsering, Ban Ma-bao, Renzeg Duojie, Gadeng Luosang, Rinchen Dongrub, Dorje Tashi, Xiao Feng, Hao Wang, Yongbin Yu,
• Abstract summary: We present TIB-STC, the first large-scale, expert-curated, and multi-domain dataset specifically designed to support the development and evaluation of large language models (LLMs)<n>Spanning over 11 billion tokens across literature, religion, medicine, law, and daily communication, TIB-STC preserves traditional grammar and stylistic richness.<n> Evaluation on TLUE Benchmark for Tibetan-specific tasks, including Ti-MMLU and Ti-SafetyBench, demonstrates the TIB-STC's effectiveness in enabling robust instruction-following and culturally aligned generation.
• Score: 10.77750944881769
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Advancement of large language models (LLMs) has brought transformative capabilities to NLP, but such progress remains unevenly distributed, especially for low-resource and culturally rich languages like Tibetan. In this paper, we present TIB-STC, the first large-scale, expert-curated, and multi-domain dataset specifically designed to support the development and evaluation of LLMs for the Tibetan language. Spanning over 11 billion tokens across literature, religion, medicine, law, and daily communication, TIB-STC preserves traditional grammar and stylistic richness. To validate its utility, we train a reference model, Sun-Shine, on TIB-STC through a three-stage pipeline involving pretraining, supervised fine-tuning, and preference optimization. Evaluation on TLUE Benchmark for Tibetan-specific tasks, including Ti-MMLU and Ti-SafetyBench, demonstrates the TIB-STC's effectiveness in enabling robust instruction-following and culturally aligned generation. We release TIB-STC to advance research in low-resource language modeling and promote inclusivity in multilingual NLP. All data are available: https://github.com/Vicentvankor/sun-shine.

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