Large language models (LLMs) have achieved remarkable advancements in natural
language understanding, generation, and manipulation of text-based data.
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 grounding from a holistic perspective
with a novel framework, AGREE, Adaptation of LLMs for GRounding EnhancEment. We
start with the design of an iterative test-time adaptation (TTA) capability
that takes into account the support information generated in self-grounded
responses. To effectively enable this capability, we tune LLMs to ground the
claims in their responses to retrieved documents by providing citations. This
tuning on top of the pre-trained LLMs requires a small amount of data that
needs to be constructed in a particular way to learn the grounding information,
for which we introduce a data construction method. Our results show that the
tuning-based AGREE framework generates better grounded responses with more
accurate citations compared to prompting-based approaches.

这篇论文关注通过一种名为 AGREE 的新框架，综合地改善大型语言模型在真实世界中的应用，以解决其生成的 “幻觉” 答案不符事实的问题。通过在生成的自身立足回答中考虑所生成的支持信息，设计了一种迭代的测试时间调整能力来有效实现该框架，并通过提供引用来调整大型语言模型对检索文档中主张的立足。结果表明，基于调整的 AGREE 框架相较于基于提示的方法，生成了更好的基于立足的回答和更准确的引用。