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arXiv · 2510.13103

ESI: Epistemic Uncertainty Quantification via Semantic-preserving Intervention for Large Language Models

Mingda Li·Xinyu Li·Weinan Zhang·Longxuan Ma
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Abstract

Uncertainty Quantification (UQ) is a promising approach to improve model reliability, yet quantifying the uncertainty of Large Language Models (LLMs) is non-trivial. In this work, we establish a connection between the uncertainty of LLMs and their invariance under semantic-preserving intervention from a causal perspective. Building on this foundation, we propose a novel grey-box uncertainty quantification method that measures the variation in model outputs before and after the semantic-preserving intervention. Through theoretical justification, we show that our method provides an effective estimate of epistemic uncertainty. Our extensive experiments, conducted across various LLMs and a variety of question-answering (QA) datasets, demonstrate that our method excels not only in terms of effectiveness but also in computational efficiency.

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Mingda Li, Xinyu Li, Weinan Zhang, Longxuan Ma. 2025-10-15. ESI: Epistemic Uncertainty Quantification via Semantic-preserving Intervention for Large Language Models. https://arxiv.org/abs/2510.13103

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