As Large Language Models (LLMs) continue to advance in their ability to write human-like text, a key challenge remains around their tendency to hallucinate generating content that appears factual but is ungrounded. This issue of hallucination is arguably the biggest hindrance to safely deploying these powerful LLMs into real-world production systems that impact people's lives. The journey toward widespread adoption of LLMs in practical settings heavily relies on addressing and mitigating hallucinations. Unlike traditional AI systems focused on limited tasks, LLMs have been exposed to vast amounts of online text data during training. While this allows them to display impressive language fluency, it also means they are capable of extrapolating information from the biases in training data, misinterpreting ambiguous prompts, or modifying the information to align superficially with the input. This becomes hugely alarming when we rely on language generation capabilities for sensitive applications, such as summarizing medical records, financial analysis reports, etc. This paper presents a comprehensive survey of over 32 techniques developed to mitigate hallucination in LLMs. Notable among these are Retrieval Augmented Generation (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. Additionally, we analyze the challenges and limitations inherent in these techniques, providing a solid foundation for future research in addressing hallucinations and related phenomena within the realm of LLMs.

翻译：随着大语言模型（LLMs）在生成类人文本能力上的持续进步，一个关键挑战始终存在：它们倾向于生成看似事实性但缺乏依据的内容——即所谓"幻觉"。这一幻觉问题堪称将这些强大LLMs安全部署到影响人们生活的真实生产系统中的最大障碍。LLMs在实际场景中的广泛采用，很大程度上依赖于对幻觉问题的解决与缓解。与专注于有限任务的传统AI系统不同，LLMs在训练过程中接触了海量在线文本数据。这虽然赋予了它们令人瞩目的语言流畅性，但也意味着它们能够从训练数据的偏差中推断信息、误解模糊提示，或使信息表面迎合输入内容。当我们将语言生成能力应用于敏感场景（如总结医疗记录、金融分析报告等）时，这一问题变得尤为令人担忧。本文全面综述了32余种专为缓解LLMs幻觉而开发的技术，其中值得关注的方法包括：检索增强生成（Lewis等，2021）、知识检索（Varshney等，2023）、CoNLI（Lei等，2023）及CoVe（Dhuliawala等，2023）。此外，我们引入了一个细粒度分类体系，根据数据集利用方式、常见任务、反馈机制和检索器类型等参数对这些方法进行归类。这一分类有助于区分专门应对LLMs幻觉问题的多样化策略。同时，我们分析了这些技术固有的挑战与局限性，为未来在LLMs领域研究幻觉及相关现象奠定了坚实基础。