Explainable Hallucination Mitigation in Large Language Models: A Survey
TL;DR
This survey addresses hallucination mitigation through the lens of explainability, proposing a taxonomy that distinguishes between internal explainability and post hoc explainability and discusses the constructive role of hallucinations in creative and user‐driven applications.
Abstract
Hallucinations in large language models (LLMs) present major obstacles to reliability in knowledge‐intensive and reasoning‐based tasks. While recent research has explored detection and correction techniques, a unified interpretive framework remains lacking. This survey addresses hallucination mitigation through the lens of explainability, proposing a taxonomy that distinguishes between internal explainability and post hoc explainability. We examine techniques such as attribution tracing, reasoning path construction, and prompt‐based verification, highlighting their roles in transparent diagnosis and structured control. Furthermore, we discuss the constructive role of hallucinations in creative and user‐driven applications, suggesting that context‐aware management may be more effective than blanket suppression. By synthesizing current advances, this review advocates for explainability as a foundation for trustworthy, controllable, and interpretable LLM systems. This article is categorized under: Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Artificial Intelligence Application Areas > Science and Technology