基于LLM的聊天机器人的引用增强生成
Citation-Enhanced Generation for LLM-based Chatbots
AI总结：
针对基于LLM的聊天机器人易产生幻觉内容的问题，提出无需训练的事后引用增强生成（CEG）方法，结合检索论证补全引用并重新生成响应，实验表明其在三个基准上的幻觉检测和响应再生性能优于现有方法。
摘要：
大型语言模型（LLMs）在包括集成到聊天机器人在内的多种场景中展现出强大的通用智能。然而，基于LLM的聊天机器人面临一个关键挑战：其响应可能生成幻觉内容，这极大限制了它们的适用性。已有多种缓解幻觉的尝试，如检索增强生成和基于人类反馈的强化学习，但大多数需要额外的训练和数据标注。本文提出一种结合检索论证的新型事后引用增强生成（CEG）方法。与以往专注于生成过程中预防幻觉的研究不同，我们的方法以事后方式解决这一问题。它集成了检索模块以搜索与生成内容相关的支持文档，并采用基于自然语言推理的引用生成模块。一旦生成内容中的陈述缺乏引用，我们的模型会重新生成响应，直到所有陈述都有引用支持。请注意，我们的方法是无需训练的即插即用插件，适用于多种LLM。在多个幻觉相关数据集上的实验表明，我们的框架在三个基准上的幻觉检测和响应再生方面均优于现有方法。我们的代码和数据集将公开可用。
英文摘要：
Large language models (LLMs) exhibit powerful general intelligence across diverse scenarios, including their integration into chatbots. However, a vital challenge of LLM-based chatbots is that they may produce hallucinated content in responses, which significantly limits their applicability. Various efforts have been made to alleviate hallucination, such as retrieval augmented generation and reinforcement learning with human feedback, but most of them require additional training and data annotation. In this paper, we propose a novel post-hoc Citation-Enhanced Generation (CEG) approach combined with retrieval argumentation. Unlike previous studies that focus on preventing hallucinations during generation, our method addresses this issue in a post-hoc way. It incorporates a retrieval module to search for supporting documents relevant to the generated content, and employs a natural language inference-based citation generation module. Once the statements in the generated content lack of reference, our model can regenerate responses until all statements are supported by citations. Note that our method is a training-free plug-and-play plugin that is capable of various LLMs. Experiments on various hallucination-related datasets show our framework outperforms state-of-the-art methods in both hallucination detection and response regeneration on three benchmarks. Our codes and dataset will be publicly available.
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。
