@inproceedings{wang-etal-2025-sconu,
title = "{SC}on{U}: Selective Conformal Uncertainty in Large Language Models",
author = "Wang, Zhiyuan and
Wang, Qingni and
Zhang, Yue and
Chen, Tianlong and
Zhu, Xiaofeng and
Shi, Xiaoshuang and
Xu, Kaidi",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.934/",
doi = "10.18653/v1/2025.acl-long.934",
pages = "19052--19075",
ISBN = "979-8-89176-251-0",
abstract = "As large language models are increasingly utilized in real-world applications, guarantees of task-specific metrics are essential for their reliable deployment. Previous studies have introduced various criteria of conformal uncertainty grounded in split conformal prediction, which offer user-specified correctness coverage. However, existing frameworks often fail to identify uncertainty data outliers that violate the exchangeability assumption, leading to unbounded miscoverage rates and unactionable prediction sets. In this paper, we propose a novel approach termed Selective Conformal Uncertainty (SConU), which, for the first time, implements significance tests, by developing two conformal p-values that are instrumental in determining whether a given sample deviates from the uncertainty distribution of the calibration set at a specific manageable risk level. Our approach not only facilitates rigorous management of miscoverage rates across both single-domain and interdisciplinary contexts, but also enhances the efficiency of predictions. Furthermore, we comprehensively analyze the components of the conformal procedures, aiming to approximate conditional coverage, particularly in high-stakes question-answering tasks."
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%0 Conference Proceedings
%T SConU: Selective Conformal Uncertainty in Large Language Models
%A Wang, Zhiyuan
%A Wang, Qingni
%A Zhang, Yue
%A Chen, Tianlong
%A Zhu, Xiaofeng
%A Shi, Xiaoshuang
%A Xu, Kaidi
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F wang-etal-2025-sconu
%X As large language models are increasingly utilized in real-world applications, guarantees of task-specific metrics are essential for their reliable deployment. Previous studies have introduced various criteria of conformal uncertainty grounded in split conformal prediction, which offer user-specified correctness coverage. However, existing frameworks often fail to identify uncertainty data outliers that violate the exchangeability assumption, leading to unbounded miscoverage rates and unactionable prediction sets. In this paper, we propose a novel approach termed Selective Conformal Uncertainty (SConU), which, for the first time, implements significance tests, by developing two conformal p-values that are instrumental in determining whether a given sample deviates from the uncertainty distribution of the calibration set at a specific manageable risk level. Our approach not only facilitates rigorous management of miscoverage rates across both single-domain and interdisciplinary contexts, but also enhances the efficiency of predictions. Furthermore, we comprehensively analyze the components of the conformal procedures, aiming to approximate conditional coverage, particularly in high-stakes question-answering tasks.
%R 10.18653/v1/2025.acl-long.934
%U https://aclanthology.org/2025.acl-long.934/
%U https://doi.org/10.18653/v1/2025.acl-long.934
%P 19052-19075
Markdown (Informal)
[SConU: Selective Conformal Uncertainty in Large Language Models](https://aclanthology.org/2025.acl-long.934/) (Wang et al., ACL 2025)
ACL
- Zhiyuan Wang, Qingni Wang, Yue Zhang, Tianlong Chen, Xiaofeng Zhu, Xiaoshuang Shi, and Kaidi Xu. 2025. SConU: Selective Conformal Uncertainty in Large Language Models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 19052–19075, Vienna, Austria. Association for Computational Linguistics.