Reinforcement Retrieval Leveraging Fine-grained Feedback for Fact Checking News Claims with Black-Box LLM

arXiv:2404.17283v1 Announce Type: new
Abstract: 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 …

abstract advanced arxiv box cs.cl feedback fine-grained however language language models language processing large language large language models llm llms natural natural language natural language processing nature nlp performance processing reinforcement retrieval retrieval-augmented signal specific tasks supervision tasks training type