Large language models (LLMs) have achieved remarkable advancements in natural language understanding and generation. However, one major issue towards their widespread deployment in the real world is that they can generate "hallucinated" answers that are not factual. Towards this end, this paper focuses on improving LLMs by grounding their responses in retrieved passages and by providing citations. We propose a new framework, AGREE, Adaptation for GRounding EnhancEment, that improves the grounding from a holistic perspective. Our framework tunes LLMs to selfground the claims in their responses and provide accurate citations to retrieved documents. This tuning on top of the pre-trained LLMs requires well-grounded responses (with citations) for paired queries, for which we introduce a method that can automatically construct such data from unlabeled queries. The selfgrounding capability of tuned LLMs further grants them a test-time adaptation (TTA) capability that can actively retrieve passages to support the claims that have not been grounded, which iteratively improves the responses of LLMs. Across five datasets and two LLMs, our results show that the proposed tuningbased AGREE framework generates superior grounded responses with more accurate citations compared to prompting-based approaches and post-hoc citing-based approaches

翻译：大语言模型在自然语言理解与生成方面取得了显著进展。然而，其在现实世界中大规模部署面临的一大问题是可能生成不符合事实的"幻觉"答案。为此，本文聚焦于通过将模型响应锚定于检索段落并提供引文来改进大语言模型。我们提出新框架AGREE（Adaptation for GRounding EnhancEment），从全局视角增强模型的接地能力。该框架通过微调使大语言模型能够自主锚定响应中的主张，并为检索文档提供准确引文。这种基于预训练大语言模型的微调需要针对查询-响应配对提供良好的接地响应（含引文），对此我们提出一种能从无标签查询自动构建此类数据的方法。微调后模型的自主接地能力进一步赋予其测试时自适应能力，能够主动检索段落以支撑尚未锚定的主张，从而迭代优化模型响应。在五个数据集和两种大语言模型上的实验表明，与基于提示的方法和事后引文方法相比，所提出的基于微调的AGREE框架能生成更优质的接地响应，且引文准确度更高。