@inproceedings{wang-etal-2024-conu,
title = "{C}on{U}: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees",
author = "Wang, Zhiyuan and
Duan, Jinhao and
Cheng, Lu and
Zhang, Yue and
Wang, Qingni and
Shi, Xiaoshuang and
Xu, Kaidi and
Shen, Heng Tao and
Zhu, Xiaofeng",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.404/",
doi = "10.18653/v1/2024.findings-emnlp.404",
pages = "6886--6898",
abstract = "Uncertainty quantification (UQ) in natural language generation (NLG) tasks remains an open challenge, exacerbated by the closed-source nature of the latest large language models (LLMs). This study investigates applying conformal prediction (CP), which can transform any heuristic uncertainty notion into rigorous prediction sets, to black-box LLMs in open-ended NLG tasks. We introduce a novel uncertainty measure based on self-consistency theory, and then develop a conformal uncertainty criterion by integrating the uncertainty condition aligned with correctness into the CP algorithm. Empirical evaluations indicate that our uncertainty measure outperforms prior state-of-the-art methods. Furthermore, we achieve strict control over the correctness coverage rate utilizing 7 popular LLMs on 4 free-form NLG datasets, spanning general-purpose and medical scenarios. Additionally, the calibrated prediction sets with small size further highlights the efficiency of our method in providing trustworthy guarantees for practical open-ended NLG applications."
}
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<abstract>Uncertainty quantification (UQ) in natural language generation (NLG) tasks remains an open challenge, exacerbated by the closed-source nature of the latest large language models (LLMs). This study investigates applying conformal prediction (CP), which can transform any heuristic uncertainty notion into rigorous prediction sets, to black-box LLMs in open-ended NLG tasks. We introduce a novel uncertainty measure based on self-consistency theory, and then develop a conformal uncertainty criterion by integrating the uncertainty condition aligned with correctness into the CP algorithm. Empirical evaluations indicate that our uncertainty measure outperforms prior state-of-the-art methods. Furthermore, we achieve strict control over the correctness coverage rate utilizing 7 popular LLMs on 4 free-form NLG datasets, spanning general-purpose and medical scenarios. Additionally, the calibrated prediction sets with small size further highlights the efficiency of our method in providing trustworthy guarantees for practical open-ended NLG applications.</abstract>
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%0 Conference Proceedings
%T ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees
%A Wang, Zhiyuan
%A Duan, Jinhao
%A Cheng, Lu
%A Zhang, Yue
%A Wang, Qingni
%A Shi, Xiaoshuang
%A Xu, Kaidi
%A Shen, Heng Tao
%A Zhu, Xiaofeng
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F wang-etal-2024-conu
%X Uncertainty quantification (UQ) in natural language generation (NLG) tasks remains an open challenge, exacerbated by the closed-source nature of the latest large language models (LLMs). This study investigates applying conformal prediction (CP), which can transform any heuristic uncertainty notion into rigorous prediction sets, to black-box LLMs in open-ended NLG tasks. We introduce a novel uncertainty measure based on self-consistency theory, and then develop a conformal uncertainty criterion by integrating the uncertainty condition aligned with correctness into the CP algorithm. Empirical evaluations indicate that our uncertainty measure outperforms prior state-of-the-art methods. Furthermore, we achieve strict control over the correctness coverage rate utilizing 7 popular LLMs on 4 free-form NLG datasets, spanning general-purpose and medical scenarios. Additionally, the calibrated prediction sets with small size further highlights the efficiency of our method in providing trustworthy guarantees for practical open-ended NLG applications.
%R 10.18653/v1/2024.findings-emnlp.404
%U https://aclanthology.org/2024.findings-emnlp.404/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.404
%P 6886-6898
Markdown (Informal)
[ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees](https://aclanthology.org/2024.findings-emnlp.404/) (Wang et al., Findings 2024)
ACL
- Zhiyuan Wang, Jinhao Duan, Lu Cheng, Yue Zhang, Qingni Wang, Xiaoshuang Shi, Kaidi Xu, Heng Tao Shen, and Xiaofeng Zhu. 2024. ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 6886–6898, Miami, Florida, USA. Association for Computational Linguistics.