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

• URL: http://arxiv.org/abs/2510.13103v1
• Date: Wed, 15 Oct 2025 02:46:43 GMT
• Title: ESI: Epistemic Uncertainty Quantification via Semantic-preserving Intervention for Large Language Models
• Authors: Mingda Li, Xinyu Li, Weinan Zhang, Longxuan Ma,
• Abstract summary: Uncertainty Quantification (UQ) is a promising approach to improve model reliability, yet the uncertainty of Large Language Models (LLMs) is non-trivial.<n>We propose a novel grey-box uncertainty quantification method that measures the variation in model outputs before and after semantic-preserving intervention.
• Score: 23.44710972442814
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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