利用细粒度反馈的强化检索，基于黑盒LLM进行新闻声明事实核查
Reinforcement Retrieval Leveraging Fine-grained Feedback for Fact Checking News Claims with Black-Box LLM
AI总结：
提出FFRR方法，通过两级策略从黑盒LLM获取细粒度反馈作为奖励优化检索策略，在新闻声明事实核查任务上显著优于LLM和非LLM基线。
摘要：
检索增强语言模型在自然语言处理（NLP）的各个领域（包括事实关键型任务）中展现出良好的性能。然而，由于先进的大型语言模型（LLMs）的黑盒特性以及特定任务的非检索导向监督信号，在黑盒LLM设置下，检索模型的训练面临重大挑战。我们提出了一种利用细粒度反馈与强化检索（FFRR）的方法，通过使用黑盒LLM来增强对新闻声明的事实核查。FFRR采用两级策略从LLM收集细粒度反馈，该反馈作为优化检索策略的奖励，通过基于任务的非检索真实情况对检索到的文档进行评分。我们在两个公共数据集上评估了我们的模型，用于现实世界的新闻声明验证，结果表明，FFRR相对于强大的启用LLM和非LLM基线取得了显著改进。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
