@inproceedings{wang-etal-2025-realm,
    title = "{REALM}: Recursive Relevance Modeling for {LLM}-based Document Re-Ranking",
    author = "Wang, Pinhuan  and
      Xia, Zhiqiu  and
      Liao, Chunhua  and
      Wang, Feiyi  and
      Liu, Hang",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1218/",
    pages = "23875--23889",
    ISBN = "979-8-89176-332-6",
    abstract = "Large Language Models (LLMs) have shown strong capabilities in document re-ranking, a key component in modern Information Retrieval (IR) systems. However, existing LLM-based approaches face notable limitations, including ranking uncertainty, unstable top-$k$ recovery, and high token cost due to token-intensive prompting. To effectively address these limitations, we propose REALM, an uncertainty-aware re-ranking framework that models LLM-derived relevance as Gaussian distributions and refines them through recursive Bayesian updates. By explicitly capturing uncertainty and minimizing redundant queries, REALM achieves better rankings more efficiently. Experimental results demonstrate that our REALM surpasses state-of-the-art re-rankers while significantly reducing token usage and latency, improving NDCG@10 by $0.7-11.9$ and simultaneously reducing the number of LLM inferences by $23.4-84.4\%$, promoting it as the next-generation re-ranker for modern IR systems."
}