A Survey of Hallucination in Large Foundation Models

TL;DR

This survey comprehensively reviews hallucination in large foundation models, covering its types, evaluation methods, mitigation strategies, and future research directions to address the challenge of factual inaccuracies.

Contribution

It provides a detailed classification of hallucination phenomena, evaluates existing mitigation approaches, and outlines future research directions for large foundation models.

Findings

Classification of hallucination types in LFMs

Evaluation criteria for hallucination assessment

Overview of mitigation strategies

Abstract

Hallucination in a foundation model (FM) refers to the generation of content that strays from factual reality or includes fabricated information. This survey paper provides an extensive overview of recent efforts that aim to identify, elucidate, and tackle the problem of hallucination, with a particular focus on ``Large'' Foundation Models (LFMs). The paper classifies various types of hallucination phenomena that are specific to LFMs and establishes evaluation criteria for assessing the extent of hallucination. It also examines existing strategies for mitigating hallucination in LFMs and discusses potential directions for future research in this area. Essentially, the paper offers a comprehensive examination of the challenges and solutions related to hallucination in LFMs.