FTibSuite: A Comprehensive Resource Suite for Tibetan Vision-Language Modeling
On the map: Multimodal LLMs / vision-language models → VLM benchmarks and visual reasoning
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 17 of its 30 references that are in the map. First posted on arXiv 26 May 2026.
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17 of 30 references.
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Other references · 16
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2020Scaling Laws for Neural Language Modelscited by 6,411
- 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
- 2021Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervisioncited by 3,848
- 2023InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuningcited by 2,855
- 2023MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Modelscited by 1,624
- 2020Don't Stop Pretraining: Adapt Language Models to Domains and Taskscited by 1,548
- 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,290
- 2022PaLI: A Jointly-Scaled Multilingual Language-Image Modelcited by 762
- 2017AI Challenger : A Large-scale Dataset for Going Deeper in Image Understandingcited by 73
- 2024Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuningcited by 73
4 more
- 2024Breaking Language Barriers: Cross-Lingual Continual Pre-Training at Scalecited by 15
- 2025Enhancing Multimodal Continual Instruction Tuning with BranchLoRAcited by 14
- 2025Adapting Large Language Models to Low-Resource Tibetan: A Two-Stage Continual and Supervised Fine-Tuning Studycited by 2
- 2023BinaryVQA: A Versatile Test Set to Evaluate the Out-of-Distribution Generalization of VQA Modelscited by 1
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Citing papers per year: 2026: 1.
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- 2026Multilingual Training and Evaluation Resources for Vision-Language Models
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- 2025Rethinking Multilingual Vision-Language Translation: Dataset, Evaluation, and Adaptation