Verbal-R3：作为检索与推理之间缺失桥梁的语义重排序
Verbal-R3: Verbal Reranker as the Missing Bridge between Retrieval and Reasoning
AI总结：
本文提出Verbal-R3框架，通过语义注释增强LLM的推理能力，实现检索与推理的高效整合，提升问答任务性能。
摘要：
传统的检索增强生成（RAG）范式将原始检索文本注入大语言模型（LLM）的上下文，常导致检索信息整合不充分。本文提出通过语义注释（Verbal Annotations）连接检索结果与LLM的推理能力。我们实证研究表明，语义注释能显著提升LLM生成准确且上下文相关的响应能力。受此启发，我们引入Verbal-R3，一种新的代理RAG框架，包含生成器和语义重排序器。生成器执行迭代检索与推理，而语义重排序器返回相关性评分和语义注释以指导生成器的推理和回答过程。通过相关性引导的测试时间扩展，进一步优化Verbal-R3的推理过程。Verbal-R3在复杂问答基准上取得最佳性能，验证了所提框架的有效性。
英文摘要：
The conventional Retrieval-Augmented Generation (RAG) paradigm of injecting raw retrieved texts into the Large Language Model (LLM)'s context often results in suboptimal integration of retrieved information. This paper proposes to bridge retrieval results and the LLM's reasoning ability through Verbal Annotations, analytic narratives that explicitly articulate the logical connection between a search query and retrieved contexts. Our empirical investigation reveals the potential of Verbal Annotations to substantially enhance the LLM's ability to generate accurate, contextually-grounded responses. Motivated by this finding, we introduce Verbal-R3, a novel agentic RAG framework that consists of a Generator and a Verbal Reranker. The Generator performs iterative retrieval and reasoning, while the Verbal Reranker returns relevance scores and Verbal Annotations to guide the reasoning and answering process of the Generator. The inference process of Verbal-R3 is further refined through relevance-guided test-time scaling, which efficiently allocates test-time compute for effective trajectory expansion. Verbal-R3 achieves state-of-the-art performance on complex Question Answering benchmarks, validating the effectiveness of the proposed framework.
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。
