# Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3 Source: [Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:55:51.510Z Authors: Xinyue Huang, Ziqi Lin, Fang Sun, Wenchao Zhang, Kejian Tong, Yunbo Liu Published: 2025-06-19T05:22:18.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()