Key Points

1. Large Language Models (LLMs) face the challenge of generating factual errors, termed "hallucinations," impacting critical tasks and necessitating mitigation.

2. Mitigation techniques include Retrieval-Augmented Generation (RAG), Knowledge Retrieval, CoNLI, and CoVe, among others, with a focus on prompt engineering and model development.

3. Detection methods like mFACT, hallucination attribution, and self-contradiction recognition have been introduced to understand and address hallucination causation.

4. Techniques such as Context-Aware Decoding, Behavioral Tuning for Refusal Skills, TWEAK for hypothesis ranking, and KG representations have been proposed to improve model faithfulness.

5. Fine-tuning Language Models for Factuality, Knowledge Injection, and Teacher-Student Approaches aim to improve the performance of weaker LLMs in specific domains.

6. Future avenues include developing hybrid models, exploring unsupervised learning techniques, and considering the social and moral implications of hallucination mitigation strategies.

Summary

The paper explores the issue of hallucination in Large Language Models (LLMs), which refers to the generation of factually incorrect information. This is a significant challenge given the widespread use of LLMs in various domains, such as text generation, question answering, and summarization. The paper presents a comprehensive survey of over thirty-two techniques developed to mitigate hallucination in LLMs, categorizing them based on various parameters such as dataset utilization, common tasks, feedback mechanisms, and retrieval types.

Taxonomy and Discussion
The paper introduces a detailed taxonomy categorizing these mitigation methods and synthesizes the essential features characterizing these techniques. It also discusses the limitations and challenges inherent in these techniques, providing a solid foundation for future research in addressing hallucinations and related phenomena within the realm of LLMs.

Mitigation Strategies
The proposed strategies to mitigate hallucination in language models include knowledge retrieval, decomposition and query framework, self-reflection methodology, loss weighting method, retrieval-augmented generation, and more. The paper also discusses the use of retrieval-augmented generation, knowledge retrieval, and real-time verification and rectification to address hallucination during the generation process.

Importance of Mitigation Strategies
The paper emphasizes the importance of developing effective strategies to address hallucination in LLMs and highlights the contributions of various techniques such as prompting GPT-3 to be reliable, ChatProtect, and self-reflection methodology in reducing hallucinations in medical generative QA systems. It also discusses the implications of these mitigation techniques in various fields.

In conclusion, the paper provides a comprehensive overview of the strategies used to mitigate hallucination in LLMs, their implications in various fields, and the challenges faced in addressing hallucination in language models. It aims to foster trust and reliability in language generation systems and provides a solid foundation for future research in this area.

Reference: https://arxiv.org/abs/2401.01313