FTibSuite: A Comprehensive Resource Suite for Tibetan Vision-Language Modeling
Vision-language models have progressed rapidly, but Tibetan remains a severely underserved low-resource language due to the lack of reproducible training and evaluation infrastructure. To fill this gap, we introduce FTibSuite, a comprehensive resource suite for Tibetan vision-language research, consisting of FTibData (human-verified multimodal training corpora spanning continual pretraining, image-text alignment, and instruction tuning data), FTibBench (Tibetan adaptations of five mainstream multimodal benchmarks with a hierarchical quality-control workflow to reduce translation noise), and FTibVLM, a reproducible baseline built on Qwen3-VL-8B-Instruct via a three-stage adaptation pipeline. Experiments on FTibBench show FTibVLM delivers consistent performance gains across all tasks, such as improving MMBench accuracy from 42.97 to 67.78 and POPE-random accuracy from 47.53 to 80.56, while retaining the backbone's original Chinese capabilities with minimal degradation, providing the first standardized foundation for Tibetan multimodal research.
Code (0)
등록된 구현이 없습니다.
Tasks
Continual PretrainingSimilar Papers 제목 키워드 기반
Tibetan Language and AI: A Comprehensive Survey of Resources, Methods and Challenges
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 developin…
Cross-Lingual TransferMachine TranslationSpeech RecognitionTLUE: A Tibetan Language Understanding Evaluation Benchmark
Large language models (LLMs) have made tremendous progress in recent years, but low-resource languages, such as Tibetan, remain significantly underrepresented in their evaluation. Despite Tibetan being spoken by over sev…
Sun-Shine: A Large Language Model for Tibetan Culture
Tibetan, a minority language in China, features a highly intricate grammatical structure, characterized by four verb tenses and a tense system with frequent irregularities, contributing to its extensive inflectional dive…
Language ModelingLanguage ModellingLarge Language ModelMachine Translation+2From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan
Large language models (LLMs) have achieved remarkable success across a wide range of natural language processing tasks, yet their performance remains heavily biased toward high-resource languages. Tibetan, despite its cu…
A Aelf-supervised Tibetan-chinese Vocabulary Alignment Method Based On Adversarial Learning
Tibetan is a low-resource language. In order to alleviate the shortage of parallel corpus between Tibetan and Chinese, this paper uses two monolingual corpora and a small number of seed dictionaries to learn the semi-sup…