Kavli Affiliate: Wei Gao
| First 5 Authors: Xuan Zhang, Wei Gao, , ,
| Summary:
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.
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