Papers by Pinhuan Wang
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. |