Tibetan Language and AI: A Comprehensive Survey of Resources, Methods and Challenges

TL;DR

This paper surveys the current state of Tibetan language AI, highlighting data resources, methods, challenges, and future directions to foster inclusive AI development for low-resource languages.

Contribution

It provides the first comprehensive categorization and evaluation of Tibetan AI resources, methods, and challenges, serving as a foundational reference for future research.

Findings

Limited datasets and benchmarks hinder progress.

Orthographic variation and data sparsity are key challenges.

Cross-lingual transfer and community efforts show promise.

Abstract

Tibetan, one of the major low-resource languages in Asia, presents unique linguistic and sociocultural characteristics that pose both challenges and opportunities for AI research. Despite increasing interest in developing AI systems for underrepresented languages, Tibetan has received limited attention due to a lack of accessible data resources, standardized benchmarks, and dedicated tools. This paper provides a comprehensive survey of the current state of Tibetan AI in the AI domain, covering textual and speech data resources, NLP tasks, machine translation, speech recognition, and recent developments in LLMs. We systematically categorize existing datasets and tools, evaluate methods used across different tasks, and compare performance where possible. We also identify persistent bottlenecks such as data sparsity, orthographic variation, and the lack of unified evaluation metrics. Additionally, we discuss the potential of cross-lingual transfer, multi-modal learning, and community-driven resource creation. This survey aims to serve as a foundational reference for future work on Tibetan AI research and encourages collaborative efforts to build an inclusive and sustainable AI ecosystem for low-resource languages.