A Concise Review of Hallucinations in LLMs and their Mitigation

TL;DR

This paper provides a concise overview of hallucinations in large language models, exploring their types, origins, and mitigation strategies to improve reliability in natural language processing.

Contribution

It offers a comprehensive summary of hallucination phenomena in LLMs and reviews current methods for their mitigation, serving as a useful resource for researchers.

Findings

Different types of hallucinations identified

Overview of mitigation techniques provided

Highlights the importance of addressing hallucinations

Abstract

Traditional language models face a challenge from hallucinations. Their very presence casts a large, dangerous shadow over the promising realm of natural language processing. It becomes crucial to understand the various kinds of hallucinations that occur nowadays, their origins, and ways of reducing them. This document provides a concise and straightforward summary of that. It serves as a one-stop resource for a general understanding of hallucinations and how to mitigate them.