ESI: Epistemic Uncertainty Quantification via Semantic-preserving Intervention for Large Language Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918160894001152 |
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| author | Li, Mingda Li, Xinyu Zhang, Weinan Ma, Longxuan |
| author_facet | Li, Mingda Li, Xinyu Zhang, Weinan Ma, Longxuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13103 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ESI: Epistemic Uncertainty Quantification via Semantic-preserving Intervention for Large Language Models Li, Mingda Li, Xinyu Zhang, Weinan Ma, Longxuan Computation and Language Artificial Intelligence Machine Learning 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. |
| title | ESI: Epistemic Uncertainty Quantification via Semantic-preserving Intervention for Large Language Models |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2510.13103 |