Theoretical Foundations and Mitigation of Hallucination in Large Language Models

• URL: http://arxiv.org/abs/2507.22915v1
• Date: Sun, 20 Jul 2025 15:22:34 GMT
• Title: Theoretical Foundations and Mitigation of Hallucination in Large Language Models
• Authors: Esmail Gumaan,
• Abstract summary: Hallucination in Large Language Models (LLMs) refers to the generation of content that is not faithful to the input or the real-world facts.<n>This paper provides a rigorous treatment of hallucination in LLMs, including formal definitions and theoretical analyses.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hallucination in Large Language Models (LLMs) refers to the generation of content that is not faithful to the input or the real-world facts. This paper provides a rigorous treatment of hallucination in LLMs, including formal definitions and theoretical analyses. We distinguish between intrinsic and extrinsic hallucinations, and define a \textit{hallucination risk} for models. We derive bounds on this risk using learning-theoretic frameworks (PAC-Bayes and Rademacher complexity). We then survey detection strategies for hallucinations, such as token-level uncertainty estimation, confidence calibration, and attention alignment checks. On the mitigation side, we discuss approaches including retrieval-augmented generation, hallucination-aware fine-tuning, logit calibration, and the incorporation of fact-verification modules. We propose a unified detection and mitigation workflow, illustrated with a diagram, to integrate these strategies. Finally, we outline evaluation protocols for hallucination, recommending datasets, metrics, and experimental setups to quantify and reduce hallucinations. Our work lays a theoretical foundation and practical guidelines for addressing the crucial challenge of hallucination in LLMs.

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