A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
Lei Huang, Weijiang Yu, Weitao Ma et al.
2024 · ACM Transactions on Information Systems · 1,421 citations
The emergence of large language models (LLMs) has marked a significant breakthrough in natural language processing (NLP), fueling a paradigm shift in information acquisition. Nevertheless, LLMs are prone to hallucination, generating plausible yet nonfactual content. This phenomenon raises significant concerns over the reliability of LLMs in real-world information retrieval (IR) systems and has attracted intensive research to detect and mitigate such hallucinations. Given the open-ended general-purpose attributes inherent to LLMs, LLM hallucinations present distinct challenges that diverge fro…
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