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English(EN) SEARCH-R: Structured Entity-Aware Retrieval with Chain-of-Reasoning Navigator for Multi-hop Question Answering

SEARCH-R框架通过实体感知检索和推理改进多跳问答

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种新颖的框架SEARCH-R，旨在通过解决推理路径生成和知识检索方面的挑战来改进多跳问答。该系统利用微调后的Llama3.1-8B模型作为推理路径导航器和子问题分解器。此外，它还采用了一种基于依赖树的检索方法，以量化评估文档的信息价值，旨在克服现有基于提示和依赖相似度评分方法的局限性。 AI

影响 通过提高推理路径生成和知识检索的准确性，增强了多跳问答系统。

排序理由 这是一篇详细介绍多跳问答新框架的研究论文。

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

SEARCH-R框架通过实体感知检索和推理改进多跳问答

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这是一篇详细介绍多跳问答新框架的研究论文。
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2 independent sources
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paper, model release
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179 days old
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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Yuqing Fu, Yimin Deng, Wanyu Wang, Yuhao Wang, Yejing Wang, Hongshi Liu, Yiqi Wang, Xiao Han, Maolin Wang, Guoshuai Zhao, Yi Chang, Xiangyu Zhao ·

    SEARCH-R：用于多跳问答的结构化实体感知检索与推理链导航器

    arXiv:2604.24515v1 Announce Type: new Abstract: Multi-hop Question Answering (MHQA) aims to answer questions that require multi-step reasoning. It presents two key challenges: generating correct reasoning paths in response to the complex user queries, and accurately retrieving es…

  2. arXiv cs.CL TIER_1 English(EN) · Xiangyu Zhao ·

    SEARCH-R：用于多跳问答的结构化实体感知检索与链式推理导航器

    Multi-hop Question Answering (MHQA) aims to answer questions that require multi-step reasoning. It presents two key challenges: generating correct reasoning paths in response to the complex user queries, and accurately retrieving essential knowledge in the face of potential limit…

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