DataProphet: Demystifying Supervision Data Generalization in Multimodal LLMs
Conventional wisdom for selecting supervision data for multimodal large language models (MLLMs) is to prioritize datasets that appear similar to the target benchmark, such as text-intensive or vision-centric tasks. However, it remains unclear whether such intuitive similarity reliably predicts downstream performance gains. In this work, we take a first step toward answering a practical question: can we estimate the influence of a training dataset on a target benchmark before any training is performed? To investigate this question, we conduct an in-depth analysis of transfer across 14 vision-language datasets spanning 7 diverse tasks. Our results show that intuitive task similarity is an u...
| Authors | Xuan Qi, Luxi He, Dan Roth et al. |
|---|---|
| Primary category | cs.CL |
| Published | 20 March 2026 |
| Last updated at source | 20 March 2026 |
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