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 \textbf{C}itation-\textbf{E}nhanced \textbf{G}eneration
(\textbf{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.

我们提出了一种新颖的后置 	extbf {引用增强生成（	extbf {CEG}）} 方法，结合检索论证。与先前的研究不同，我们的方法并不着重于在生成过程中防止幻觉，而是采用后置方式解决这个问题。我们的模型包括一个检索模块，用于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文生成模块。如果生成内容中的陈述缺乏参考文献，我们的模型可以重复生成回复，直到所有陈述都有引文支持。在各种与幻觉相关的数据集上的实验证明，我们的框架在幻觉检测和回复重构两个基准上优于现有方法。我们的代码和数据集将公开提供。