Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3
This addresses document-level QA challenges for users needing precise answers from long texts, but appears incremental as it builds on established RAG and LLaMA methods.
This paper tackled the problem of complex question answering across lengthy documents by developing a Retrieval-Augmented Generation framework with multi-hop reasoning, built on LLaMA 3, and reported that it outperformed existing baselines.
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.