  Large language models (LLMs) exhibit remarkable performance across various
NLP tasks. However, they often generate incorrect or hallucinated information,
which hinders their practical applicability in real-world scenarios. Human
feedback has been shown to effectively enhance the factuality and quality of
generated content, addressing some of these limitations. However, this approach
is resource-intensive, involving manual input and supervision, which can be
time-consuming and expensive. Moreover, it cannot be provided during inference,
further limiting its practical utility in dynamic and interactive applications.
In this paper, we introduce ReFeed, a novel pipeline designed to enhance LLMs
by providing automatic retrieval feedback in a plug-and-play framework without
the need for expensive fine-tuning. ReFeed first generates initial outputs,
then utilizes a retrieval model to acquire relevant information from large
document collections, and finally incorporates the retrieved information into
the in-context demonstration for output refinement, thereby addressing the
limitations of LLMs in a more efficient and cost-effective manner. Experiments
on four knowledge-intensive benchmark datasets demonstrate our proposed ReFeed
could improve over +6.0% under zero-shot setting and +2.5% under few-shot
setting, compared to baselines without using retrieval feedback.
