@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://aclanthology.org/2025.findings-acl.1167/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from LLMs
%A Yu, Puxuan
%A Cohen, Daniel
%A Lamba, Hemank
%A Tetreault, Joel R.
%A Jaimes, Alejandro
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F yu-etal-2025-explain
%X 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.
%R 10.18653/v1/2025.findings-acl.1167
%U https://aclanthology.org/2025.findings-acl.1167/
%U https://doi.org/10.18653/v1/2025.findings-acl.1167
%P 22716-22730
Markdown (Informal)
[Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from LLMs](https://aclanthology.org/2025.findings-acl.1167/) (Yu et al., Findings 2025)
ACL