Hallucination Detection and Hallucination Mitigation: An Investigation

TL;DR

This paper reviews current methods for detecting and reducing hallucinations in large language models, aiming to guide researchers and engineers in improving LLM reliability for real-world applications.

Contribution

It provides a comprehensive overview of existing techniques for hallucination detection and mitigation in LLMs, highlighting current challenges and future directions.

Findings

Summarizes key detection methods for hallucinations.

Reviews mitigation strategies to reduce hallucinations.

Identifies gaps and future research directions.

Abstract

Large language models (LLMs), including ChatGPT, Bard, and Llama, have achieved remarkable successes over the last two years in a range of different applications. In spite of these successes, there exist concerns that limit the wide application of LLMs. A key problem is the problem of hallucination. Hallucination refers to the fact that in addition to correct responses, LLMs can also generate seemingly correct but factually incorrect responses. This report aims to present a comprehensive review of the current literature on both hallucination detection and hallucination mitigation. We hope that this report can serve as a good reference for both engineers and researchers who are interested in LLMs and applying them to real world tasks.