Large language models (LLMs) can generate fluent natural language texts when given relevant documents as background context. This ability has attracted considerable interest in developing industry applications of LLMs. However, LLMs are prone to generate hallucinations that are not supported by the provided sources. In this paper, we propose a hierarchical framework to detect and mitigate such ungrounded hallucination. Our framework uses Chain of Natural Language Inference (CoNLI) for hallucination detection and hallucination reduction via post-editing. Our approach achieves state-of-the-art performance on hallucination detection and enhances text quality through rewrite, using LLMs without any fine-tuning or domain-specific prompt engineering. We show that this simple plug-and-play framework can serve as an effective choice for hallucination detection and reduction, achieving competitive performance across various contexts.

翻译：大型语言模型（LLMs）在给定相关文档作为背景上下文时，能够生成流畅的自然语言文本。这一能力引发了开发LLMs行业应用的浓厚兴趣。然而，LLMs容易产生不被提供来源所支持的幻觉。本文提出一种层次化框架，用于检测并缓解此类无据幻觉。该框架采用自然语言推理链（CoNLI）进行幻觉检测，并通过后期编辑减少幻觉。我们的方法在幻觉检测上达到了最先进水平，并通过重写提升了文本质量，无需对LLMs进行微调或针对特定领域的提示工程。研究表明，这一简单的即插即用框架可作为幻觉检测与减少的有效选择，在多种上下文中均表现出竞争性性能。