大型基础模型中的幻觉综述
A Survey of Hallucination in Large Foundation Models
AI总结：
本综述全面探讨了大型基础模型（LFMs）中的幻觉问题，对幻觉现象进行分类并建立评估标准，同时审查了缓解策略并指出了未来研究方向。
摘要：
基础模型（FM）中的幻觉是指生成偏离事实现实或包含捏造信息的内容。本综述论文提供了旨在识别、阐明和解决幻觉问题的近期工作的广泛概述，特别关注“大型”基础模型（LFMs）。论文对LFMs特有的各类幻觉现象进行了分类，并建立了用于评估幻觉程度的评估标准。它还审查了减轻LFMs中幻觉的现有策略，并讨论了该领域未来研究的潜在方向。本质上，本文对LFMs中与幻觉相关的挑战和解决方案进行了全面考察。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
