# Towards the Roof of the World: NLP Progress in Tibetan Languages

arXiv: https://arxiv.org/abs/2610.13398 · HTML: https://mlsanity.com/paper/2610.13398/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 171 references that are in the map.
- First posted on arXiv 9 Oct 2026.

## Builds on (most cited first, 22 of 22)

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- [TLUE: A Tibetan Language Understanding Evaluation Benchmark](https://mlsanity.com/paper/2503.12051/) (2025)
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- [Pay Attention to the Robustness of Chinese Minority Language Models! Syllable-level Textual Adversarial Attack on Tibetan Script](https://mlsanity.com/paper/2412.02323/) (2024)
- [From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan](https://mlsanity.com/paper/2507.09205/) (2025)
- [Cleansing Jewel: A Neural Spelling Correction Model Built On Google OCR-ed Tibetan Manuscripts](https://mlsanity.com/paper/2304.03427/) (2023)
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- [Reinforcement Learning with Semantic Rewards Enables Low-Resource Language Expansion without Alignment Tax](https://mlsanity.com/paper/2605.14366/) (2026)
- [TSCheater: Generating High-Quality Tibetan Adversarial Texts via Visual Similarity](https://mlsanity.com/paper/2412.02371/) (2024)
- [Human-in-the-Loop Generation of Adversarial Texts: A Case Study on Tibetan Script](https://mlsanity.com/paper/2412.12478/) (2024)
- [FTibSuite: A Comprehensive Resource Suite for Tibetan Vision-Language Modeling](https://mlsanity.com/paper/2605.26601/) (2026)
- [CUTE: A Multilingual Dataset for Enhancing Cross-Lingual Knowledge Transfer in Low-Resource Languages](https://mlsanity.com/paper/2509.16914/) (2025)
- [The Tonogenesis Continuum in Tibetan: A Computational Investigation](https://mlsanity.com/paper/2510.22485/) (2025)
- [NepTam: A Nepali-Tamang Parallel Corpus and Baseline Machine Translation Experiments](https://mlsanity.com/paper/2603.14053/) (2026)
- [BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resources](https://mlsanity.com/paper/2604.18423/) (2026)

## Abstract (arXiv)

Tibetan, spoken by some seven million people on and around the Qinghai-Xizang Plateau, remains underrepresented in natural language processing (NLP). We present, to our knowledge, the first peer-reviewed systematic bibliometric survey of Tibetan NLP: 140 publications from 2004 to 2026 in the ACL Anthology and DBLP, annotated for venue type, research area, first-affiliation country, and resource release. Output has grown rapidly, with 84% of the publications appearing since 2018, and has expanded bottom-up along the NLP pipeline, from corpora and word segmentation through script, speech, and applications to large language models (LLMs). Researchers based in China first-authored 87% of the publications, which span all 25 represented research areas, whereas work from other countries concentrates on Classical Tibetan. We argue that this full-pipeline paradigm, realized under the compounded constraints of a complex script, dialectal diversity, and geographic remoteness, offers a reference model for other low-resource languages of the Global South. We also identify gaps in dialect coverage, responsible NLP, native and comparable evaluation, cross-community visibility, and resource release (only 29% of the publications release data, code, or models), and recommend open science, shared standards, and community participation for the LLM era.