A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models

This paper presents a comprehensive survey of over thirty-two techniques developed to mitigate hallucination in LLMs. Notable among these are Retrieval-Augmented Generation (RAG) (Lewis et al., 2021), Knowledge Retrieval (Varshney et al., 2023), CoNLI (Lei et al., 2023), and CoVe (Dhuliawala et al., 2023). Furthermore, we introduce a detailed taxonomy categorizing these methods based on various parameters, such as dataset utilization, common tasks, feedback mechanisms, and retriever types. This classification helps distinguish the diverse approaches specifically designed to tackle hallucination issues in LLMs.