A Concise Review of Hallucinations in LLMs and their Mitigation

TL;DR

Hallucinations in LLMs pose a critical reliability challenge, particularly in high-stakes domains. The paper surveys typologies, origins, and mitigation strategies, spanning detection, evaluation, domain adaptation, and prompt/post-processing techniques. It highlights that a combination of retrieval-augmented generation, external knowledge integration, and alignment training (including RLHF) improves factuality and trust, with domain-specific considerations. The authors call for real-time hallucination detection, multimodal assessment, and formal-methods-based guarantees to advance safe, reliable AI text systems.

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.

Figures (2)

• Figure 1: Hallucination In Language Models
• Figure 2: Comparison of Hallucination-Mitigation Techniques Across Key Metrics. This bar chart presents accuracy (blue bars) and faithfulness (green bars) for five methods