Retrieval-augmented language models have exhibited promising performance
across various areas of natural language processing (NLP), including
fact-critical tasks. However, due to the black-box nature of advanced large
language models (LLMs) and the non-retrieval-oriented supervision signal of
specific tasks, the training of retrieval model faces significant challenges
under the setting of black-box LLM. We propose an approach leveraging
Fine-grained Feedback with Reinforcement Retrieval (FFRR) to enhance
fact-checking on news claims by using black-box LLM. FFRR adopts a two-level
strategy to gather fine-grained feedback from the LLM, which serves as a reward
for optimizing the retrieval policy, by rating the retrieved documents based on
the non-retrieval ground truth of the task. We evaluate our model on two public
datasets for real-world news claim verification, and the results demonstrate
that FFRR achieves significant improvements over strong LLM-enabled and non-LLM
baselines.

使用黑盒语言模型的精细反馈和增强检索，增强了新闻索赔事实验证的结果，并在真实世界的数据集上显著改进了强大的语言模型和非语言模型基线。