Improving Language Models via Plug-and-Play Retrieval Feedback
Wenhao Yu Zhihan Zhang Zhenwen Liang Meng Jiang Ashish Sabharwal
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.