# TFD: A Comprehensive Structured Tibetan Foundation Dataset for Low-Resource Language Processing and Large-Scale Modeling

arXiv: https://arxiv.org/abs/2503.18288 · HTML: https://mlsanity.com/paper/2503.18288/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in LLM preference optimization methods, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 11 of its 46 references that are in the map.
- First posted on arXiv 24 Mar 2025.

## Placed in

- LLM alignment / RLHF / preference optimisation / [LLM preference optimization methods](https://mlsanity.com/alignment/llm-preference-optimization-methods/)
- LLM safety and security / [Safety alignment, refusal and guardrails](https://mlsanity.com/safety/safety-alignment-refusal-and-guardrails/)

## Builds on (most cited first, 11 of 11)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [CMMLU: Measuring massive multitask language understanding in Chinese](https://mlsanity.com/paper/2306.09212/) (2023)
- [Understanding the Effects of RLHF on LLM Generalisation and Diversity](https://mlsanity.com/paper/2310.06452/) (2023)
- [Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach](https://mlsanity.com/paper/2010.07835/) (2020)
- [Raising the Bar: Investigating the Values of Large Language Models via Generative Evolving Testing](https://mlsanity.com/paper/2406.14230/) (2024)
- [Open Ko-LLM Leaderboard2: Bridging Foundational and Practical Evaluation for Korean LLMs](https://mlsanity.com/paper/2410.12445/) (2024)
- [TLUE: A Tibetan Language Understanding Evaluation Benchmark](https://mlsanity.com/paper/2503.12051/) (2025)
- [MiLiC-Eval: Benchmarking Multilingual LLMs for China's Minority Languages](https://mlsanity.com/paper/2503.01150/) (2025)

## Built on by (most cited first, 3 of 3)

- [TIBSTC-CoT: A Multi-Domain Instruction Dataset for Chain-of-Thought Reasoning in Language Models](https://mlsanity.com/paper/2508.01977/) (2025)
- [Tibetan Language and AI: A Comprehensive Survey of Resources, Methods and Challenges](https://mlsanity.com/paper/2510.19144/) (2025)
- [LLM Safety Alignment in Low-Resource Languages: A Systematic Literature Review](https://mlsanity.com/paper/2608.14626/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have achieved remarkable success in high-resource languages, yet progress in Tibetan remains severely constrained. While recent efforts have begun to address pre-training data scarcity for Tibetan, a more fundamental gap persists: no existing resource supports the complete LLM development pipeline, spanning pre-training, instruction tuning, safety alignment, preference optimization, and reasoning supervision. We introduce the Tibetan Foundation Dataset (TFD), the first structured, large-scale, and expert-curated dataset covering all key stages of Tibetan large language modeling. TFD comprises TIBSTC, a unified corpus of over 11 billion tokens with curated sub-datasets for instruction tuning, safety alignment, and preference optimization, and TIBSTC-CoT, the first large-scale Tibetan chain-of-thought dataset. We demonstrate its utility by training the Sun-Shine family of Tibetan LLMs, achieving substantial improvements over strong baselines on understanding, safety, reasoning, and generation benchmarks. These results underscore that advancing low-resource language modeling requires not only scale, but a structurally complete data ecosystem. We release TFD to facilitate reproducible research and the development of robust, culturally aligned Tibetan LLMs. Code and data are available at https://github.com/Vicentvankor/sun-shine.