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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2510.19144 |
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| _version_ | 1866914107609841664 |
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| author | Huang, Cheng Tashi, Nyima Gao, Fan Liu, Yutong Li, Jiahao Tian, Hao Jiang, Siyang Tsering, Thupten Ma-bao, Ban Duojie, Renzeg Luosang, Gadeng Dongrub, Rinchen Tashi, Dorje Zhang, Jin Feng, Xiao Wang, Hao Tang, Jie Tang, Guojie Wang, Xiangxiang Zhang, Jia Lee, Tsengdar Yu, Yongbin |
| author_facet | Huang, Cheng Tashi, Nyima Gao, Fan Liu, Yutong Li, Jiahao Tian, Hao Jiang, Siyang Tsering, Thupten Ma-bao, Ban Duojie, Renzeg Luosang, Gadeng Dongrub, Rinchen Tashi, Dorje Zhang, Jin Feng, Xiao Wang, Hao Tang, Jie Tang, Guojie Wang, Xiangxiang Zhang, Jia Lee, Tsengdar Yu, Yongbin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19144 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tibetan Language and AI: A Comprehensive Survey of Resources, Methods and Challenges Huang, Cheng Tashi, Nyima Gao, Fan Liu, Yutong Li, Jiahao Tian, Hao Jiang, Siyang Tsering, Thupten Ma-bao, Ban Duojie, Renzeg Luosang, Gadeng Dongrub, Rinchen Tashi, Dorje Zhang, Jin Feng, Xiao Wang, Hao Tang, Jie Tang, Guojie Wang, Xiangxiang Zhang, Jia Lee, Tsengdar Yu, Yongbin Computation and Language 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. |
| title | Tibetan Language and AI: A Comprehensive Survey of Resources, Methods and Challenges |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2510.19144 |