A Survey of Hallucination in Large Foundation Models
By Vipula Rawte, Amit P. Sheth, Amitava Das. arXiv.org, 2023
604 citations recorded by the source corpus.
Hallucination in a foundation model (FM) refers to the generation of content that strays from factual reality or includes fabricated information. This survey paper provides an extensive overview of recent efforts that aim to identify, elucidate, and tackle the problem of hallucination, with a particular focus on ``Large'' Foundation Models (LFMs). The paper classifies various types of hallucination phenomena that are specific to LFMs and establishes evaluation criteria for assessing the extent of hallucination. It also examines existing strategies for mitigating hallucination in LFMs and discusses potential directions for future research in this area. Essentially, the paper offers a comprehensive examination of the challenges and solutions related to hallucination in LFMs.
Subfields: Cognitive Neuroscience, Economics and Econometrics, Experimental and Cognitive Psychology
View Paper: https://www.semanticscholar.org/paper/71bc0c97c20fffce796a355b16bd202987260029
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Citations in the corpus
- A Survey on Evaluation of Large Language Models (2023, background): “However, the information generated can include factual inaccuracies or statements ungrounded in reality, a phenomenon known as hallucination [154, 239].”
- A Survey on the Memory Mechanism of Large Language Model-based Agents (2024, neutral): “One of the most prominent studies is Reflexion [138], which proposes verbal reinforcement learning for LLM-based agents.”
- Understanding the Effects of Iterative Prompting on Truthfulness (2024, background): “Amidst their impressive feats, LLMs’ consistency in delivering accurate and verifiable information remains a pertinent concern [9, 10].”
- Large language models in healthcare and medical domain: A review (2023, background): “These models leverage deep learning techniques to understand and generate content across different modalities, enhancing their applicability in real-world scenarios.”
- RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models (2023, neutral): “It contains context, response generated by LLM with and span-level annotation. tual or accurate information (Rawte et al., 2023).”
- A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions (2023, neutral): “As the push for reliable generative AI intensifies, LLM hallucination stands out as a major challenge, leading to numerous surveys on its recent advancements (Ji et al., 2023a; Rawte et al., 2023; Liu et al., 2023h; Zhang et al., 2023g; Wang et al., 2023c).”