幻觉检测与幻觉缓解：一项调查研究
Hallucination Detection and Hallucination Mitigation: An Investigation
AI总结：
本文系统综述了大型语言模型幻觉检测与缓解的研究现状，旨在为相关工程师和研究人员提供参考。
摘要：
包括ChatGPT、Bard和Llama在内的大型语言模型（LLMs）在过去两年中于一系列不同应用中取得了显著成功。尽管取得了这些成功，仍存在一些限制LLMs广泛应用的担忧。一个关键问题是幻觉问题。幻觉指的是这样一个事实：除了正确回答之外，LLMs还可能生成看似正确但事实不正确的回答。本报告旨在对当前关于幻觉检测和幻觉缓解的文献进行全面综述。我们希望本报告能为对LLMs感兴趣并将其应用于现实世界任务的工程师和研究人员提供良好的参考。
英文摘要：
Large language models (LLMs), including ChatGPT, Bard, and Llama, have achieved remarkable successes over the last two years in a range of different applications. In spite of these successes, there exist concerns that limit the wide application of LLMs. A key problem is the problem of hallucination. Hallucination refers to the fact that in addition to correct responses, LLMs can also generate seemingly correct but factually incorrect responses. This report aims to present a comprehensive review of the current literature on both hallucination detection and hallucination mitigation. We hope that this report can serve as a good reference for both engineers and researchers who are interested in LLMs and applying them to real world tasks.
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。
