Preprint
SConU: Selective Conformal Uncertainty in Large Language Models
18 Apr 2025
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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Details
- Title
- SConU: Selective Conformal Uncertainty in Large Language Models
- Creators
- Zhiyuan WangQingni WangYue ZhangTianlong ChenXiaofeng ZhuXiaoshuang ShiKaidi Xu
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Computer Science
- Other Identifier
- 991022048906704721