A Survey of NLP Progress in S ino- T ibetan Low-Resource Languages (2025)

Abstract: Despite the increasing effort in including more low-resource languages in NLP/CL development, most of the world’s languages are still absent. In this paper, we take the example of the Sino-Tibetan language family which consists of hundreds of low-resource languages, and we look at the representation of these low-resource languages in papers archived on ACL Anthology. Our findings indicate that while more techniques and discussions on more languages are present in more publication venues over the years, the overall focus on this language family has been minimal. The lack of attention might be owing to the small number of native speakers and governmental support of these languages. The current development of large language models, albeit successful in a few quintessential rich-resource languages, are still trailing when tackling these low-resource languages. Our paper calls for the attention in NLP/CL research on the inclusion of low-resource languages, especially as increasing resources are poured into the development of data-driven language models.

## introduction research on low-resource languages (lrls) has become a major topic in the natural language processing (nlp) and computational linguistics (cl) as seen by promotions from various conferences and workshops @xcite , especially as we strive to develop multilingual large language models (llms) to incorporate more languages @xcite . chat-gpt @xcite , one of the most successful commercially available llms, is claimed to support more than 80 languages @xcite . however, this number is still far lower than the total number of languages in the world, which is estimated to be more than 7,000 @xcite . as @xcite pointed out, we would want to improve the linguistic diversity and inclusion in nlp/cl development, as "unsupervised pre-train methods only make the 'poor poorer'." one common way to improve the performance of language models on lrls is cross-lingual transfer learning @xcite , with which tasks in a target language are evaluated by models trained on source language(s). this technique has been found to work better when the source languages include languages from the same family as the target language @xcite . a language family is a group of "genetically" related languages which all descended from the same ancestral language @xcite . in this paper, we focus on the sino-tibetan (st) language family, with the fourth largest number of languages @xcite and the second largest number of speakers @xcite . it consists of sinitic languages such as mandarin and cantonese, and tibeto-burman languages tibetan and burmese, that descended from a hypothetical common ancestor proto-sino-tibetan. there are over 1 billion native speakers of st languages, mostly occupying "east asia, peninsular southeast asia, and parts of south asia" @xcite . a figure of the geographic distribution of sino-tibetan languages is shown in appendix a. chinese foot_0 has been the most focused st languages by the nlp family such that the late chat-gpt is able to perform well on natural language tasks @xcite and ernie bot (baidu research), a chinese llm, was developed not long after the release of chatgpt. however, it is yet unknown how other st languages are represented in the nlp literature, especially since they are all considered as lrls. our study attempts to answer on the following research questions: rq1. what is the overall and individual coverage of low-resource st languages in nlp/cl literature? ## conclusion our paper is the first in the field to provide a systematic look into the coverage of st languages in nlp/cl literature. from our analyses, we acknowledge the encouraging improvement made by the nlp/cl community to include more st languages over the years, and have a wide range of techniques and discussion topics about these languages. however, we also raise concerns on the low count of publications, covering only part of our list of languages, despite the list having already excluded hundreds of st languages with even lower number of native speakers. the publication venues have also become more diverse, but most papers possibly did not receive enough attention from the research community. the existing disparity of the literature coverage and model performance on st lrls as compared to high-resource languages may be worsened by the advancement of llms, relying on large quantity of unlabeled data, making the "poor poorer" @xcite . we hope that this paper calls for more research into st languages and ways to improve the status of lrls.