@inproceedings{yu-etal-2025-explain,
    title = "Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from {LLM}s",
    author = "Yu, Puxuan  and
      Cohen, Daniel  and
      Lamba, Hemank  and
      Tetreault, Joel R.  and
      Jaimes, Alejandro",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.1167/",
    pages = "22716--22730",
    ISBN = "979-8-89176-256-5",
    abstract = "In search settings, calibrating the scores during the ranking process to quantities such as click-through rates or relevance levels enhances a system{'}s usefulness and trustworthiness for downstream users. While previous research has improved this notion of calibration for low complexity learning-to-rank models, the larger data demands and parameter count specific to modern neural text rankers produce unique obstacles that hamper the efficacy of methods intended for the learning-to-rank setting.This paper proposes exploiting large language models (LLMs) to provide relevance and uncertainty signals for these neural text rankers to produce scale-calibrated scores through Monte Carlo sampling of natural language explanations (NLEs). Our approach transforms the neural ranking task from ranking textual query-document pairs to ranking corresponding synthesized NLEs. Comprehensive experiments on two popular document ranking datasets show that the NLE-based calibration approach consistently outperforms past calibration methods and LLM-based methods for ranking, calibration, and query performance prediction tasks."
}