不要相信你读到的一切：通过自动识别大型语言模型中的幻觉增强摘要可解释性
Don't Believe Everything You Read: Enhancing Summarization Interpretability through Automatic Identification of Hallucinations in Large Language Models
AI总结：
本文针对LLMs在对话摘要中的幻觉问题，提出令牌级幻觉识别方法，并据此构建增强数据集和新训练范式，以提升模型的可解释性与忠实性。
摘要：
大型语言模型（LLMs）擅长文本操作——例如机器翻译和文本摘要等任务。然而，这些模型也可能容易产生幻觉，这可能对模型提供的任何答案的忠实性造成损害。近期对抗LLMs幻觉的研究工作涉及识别产生幻觉的句子，并对模型产生幻觉的不同方式进行分类。本文深入探讨了LLMs在幻觉方面的行为，定义了一种令牌级方法来识别不同类型的幻觉，并进一步利用这种令牌级标注来提高LLMs在对话摘要任务中的可解释性和忠实性。通过这一方法，本文提出了一个新的、增强的数据集和一种新的训练范式。
英文摘要：
Large Language Models (LLMs) are adept at text manipulation -- tasks such as machine translation and text summarization. However, these models can also be prone to hallucination, which can be detrimental to the faithfulness of any answers that the model provides. Recent works in combating hallucinations in LLMs deal with identifying hallucinated sentences and categorizing the different ways in which models hallucinate. This paper takes a deep dive into LLM behavior with respect to hallucinations, defines a token-level approach to identifying different kinds of hallucinations, and further utilizes this token-level tagging to improve the interpretability and faithfulness of LLMs in dialogue summarization tasks. Through this, the paper presents a new, enhanced dataset and a new training paradigm.
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。
