  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
