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arXiv · 2506.16037

Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3

Xinyue Huang·Ziqi Lin·Fang Sun·Wenchao Zhang·Kejian Tong·Yunbo Liu
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Abstract

This paper presents a novel Retrieval-Augmented Generation (RAG) framework tailored for complex question answering tasks, addressing challenges in multi-hop reasoning and contextual understanding across lengthy documents. Built upon LLaMA 3, the framework integrates a dense retrieval module with advanced context fusion and multi-hop reasoning mechanisms, enabling more accurate and coherent response generation. A joint optimization strategy combining retrieval likelihood and generation cross-entropy improves the model's robustness and adaptability. Experimental results show that the proposed system outperforms existing retrieval-augmented and generative baselines, confirming its effectiveness in delivering precise, contextually grounded answers.

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Xinyue Huang, Ziqi Lin, Fang Sun, Wenchao Zhang, Kejian Tong, Yunbo Liu. 2025-06-19. Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3. https://arxiv.org/abs/2506.16037

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