Papers by Pooyan Fazli
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 . |
OSCaR: Object State Captioning and State Change Representation (2024.findings-naacl)
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| Challenge: | Existing methods to extrapolate and comprehend changes in object states are limited . relying on a small set of symbolic words to represent changes has restricted expressiveness of language. |
| Approach: | They propose a dataset and benchmark to evaluate multimodal large language models . they investigate causal relations between a concrete action and the change . |
| Outcome: | The proposed method achieves near parity with GPT-4V ratings across helpfulness, accuracy, reasoning, and other key metrics. |
ReGATE: Learning Faster and Better with Fewer Tokens in MLLMs (2026.acl-long)
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| Challenge: | Existing efficiency methods mainly target inference via token reduction or merging, offering limited benefits during training. |
| Approach: | They propose an adaptive token pruning method that uses a teacher-student framework to prune MLLMs to reduce inference costs. |
| Outcome: | The proposed method matches the peak accuracy of standard training on MVBench up to **2 faster**, using only **38% of the tokens. |