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.
@article{arxiv.2506.16037,
title = {Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3},
author = {Xinyue Huang and Ziqi Lin and Fang Sun and Wenchao Zhang and Kejian Tong and Yunbo Liu},
journal= {arXiv preprint arXiv:2506.16037},
year = {2025}
}