Papers with massive pretraining
VideoPASTA: 7K Preference Pairs That Matter for Video-LLM Alignment (2025.emnlp-main)
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| Challenge: | Video-language models excel at understanding video content but struggle with spatial relationships, temporal ordering, and cross-frame continuity. |
| Approach: | They propose a framework that trains video-LLMs to distinguish accurate representations from carefully crafted adversarial examples. |
| Outcome: | Experiments show that VideoPASTA improves performance without human annotation or captioning . the framework can be used on various state-of-the-art video-LLMs with no human annotation . |