Retrieval-Augmented Generation (RAG) has emerged as a crucial approach for enhancing the responses of large language models (LLMs) with external knowledge sources. Despite the impressive performance in complex question-answering tasks, RAG still struggles with hallucinations. Attributing RAG-generated content through in-line citations has demonstrated potential in reducing hallucinations and facilitating human verification. Existing citation generation methods primarily rely on either fine-tuning the generator or employing post-processing approaches for citation matching. However, the former approach demands substantial annotated data and computational resources, while the latter often encounters difficulties in managing multiple citations and frequently produces suboptimal results. In this paper, we introduce a novel framework, called VeriCite, designed to rigorously validate supporting evidence and enhance answer attribution. Specifically, VeriCite breaks down into a three-stage generation: 1) The initial answer generation first generates a response based on all available contexts and has its claims verified through the NLI model; 2) the supporting evidence selection assesses the utility of each document and extracts useful supporting evidences; 3) the final answer refinement integrates the initial response and collected evidences to produce the final, refined answer.We conduct experiments across five open-source LLMs and four datasets, demonstrating that VeriCite can significantly improve citation quality while maintaining the correctness of the answers.

翻译：检索增强生成（RAG）已成为利用外部知识源增强大语言模型（LLM）响应能力的关键方法。尽管在复杂问答任务中表现出色，RAG仍面临幻觉问题。通过内联引用对RAG生成内容进行归因，已显示出减少幻觉和便于人工验证的潜力。现有引用生成方法主要依赖于微调生成器或采用后处理方法进行引用匹配。然而，前者需要大量标注数据和计算资源，后者则常在处理多重引用时遇到困难，且常产生次优结果。本文提出一种名为VeriCite的新型框架，旨在严格验证支持证据并增强答案归因。具体而言，VeriCite分解为三阶段生成过程：1）初始答案生成阶段首先基于所有可用上下文生成响应，并通过NLI模型验证其主张；2）支持证据选择阶段评估每个文档的效用并提取有用的支持证据；3）最终答案优化阶段整合初始响应与收集的证据，生成经过优化的最终答案。我们在五个开源LLM和四个数据集上进行实验，证明VeriCite能在保持答案正确性的同时显著提升引用质量。