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

2025-10-21

AI Summary

This paper addresses fundamental challenges confronting Tibetan—a low-resource language in AI—including severe data scarcity, substantial orthographic variation, absence of standardized evaluation benchmarks, and underdeveloped tooling infrastructure. To tackle these issues, we present the first systematic, multimodal resource atlas and technical survey spanning natural language processing, automatic speech recognition, machine translation, and large language models. Leveraging comprehensive literature analysis and methodological taxonomy, we identify and formalize three key technical pathways: cross-lingual transfer enhancement, multimodal collaborative modeling, and community-driven co-construction. Building upon these, we establish the first unified benchmarking framework for Tibetan AI research. Our work fills a critical gap in systematic scholarly synthesis, clarifies persistent technical bottlenecks and strategic development trajectories, and delivers a reusable methodological paradigm and collaborative infrastructure—advancing both Tibetan-specific AI and broader low-resource language research.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Addressing data scarcity and standardization gaps in Tibetan AI
• Evaluating NLP and speech processing methods for Tibetan language
• Identifying challenges in cross-lingual transfer and resource development

Innovation

• Surveying Tibetan AI resources and methods comprehensively
• Evaluating NLP and speech tasks with existing datasets
• Proposing cross-lingual transfer and community-driven solutions