Papers by Feiyi Wang
HiPrune: Hierarchical Attention for Efficient Token Pruning in Vision-Language Models (2026.findings-acl)
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| Challenge: | Existing methods for visual token pruning lack insight into the intrinsic property of the vision encoder . et al., 2017: 99.3% of task accuracy with only 1/3 of the tokens. |
| Approach: | They propose a model-agnostic token pruning method that trains without training . they propose 'HiPrune' method which prunes visual tokens according to their attention . |
| Outcome: | The proposed method achieves 99.3% of task accuracy with only 1/3 of the tokens . it reduces inference FLOPs by 58.7% and maintains 99.99% accuracy with 2/9 tokens. |
REALM: Recursive Relevance Modeling for LLM-based Document Re-Ranking (2025.emnlp-main)
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| Challenge: | Existing LLMs face ranking uncertainty, unstable top-k recovery, and high token cost due to token-intensive prompting. |
| Approach: | They propose a re-ranking framework that captures uncertainty and refines LLM-derived relevance through recursive Bayesian updates. |
| Outcome: | The proposed framework outperforms state-of-the-art re-rankers while reducing token usage and latency. |