Theoretical Foundations and Mitigation of Hallucination in Large Language Models

TL;DR

This paper offers a comprehensive theoretical and practical framework for understanding, detecting, and mitigating hallucinations in large language models, combining formal analysis with survey and workflow proposals.

Contribution

It provides the first formal definitions, risk bounds, and a unified detection-mitigation workflow for hallucinations in LLMs, advancing both theory and practice.

Findings

Derived bounds on hallucination risk using PAC-Bayes and Rademacher complexity.

Surveyed effective detection strategies like uncertainty estimation and attention checks.

Discussed mitigation techniques including retrieval augmentation and fact-verification modules.

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.