用LLaMA 3增强长文档问答，支持多跳推理和上下文融合。
Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3
- 基于LLaMA 3构建多跳检索增强生成框架
- 在多个数据集上超越现有基线模型
- 适合需要深度理解长文本的问答任务
本文提出一种专为复杂问答任务设计的检索增强生成（RAG）框架，解决长文档中多跳推理与上下文理解的挑战。基于LLaMA 3，该框架集成密集检索模块与先进的上下文融合及多跳推理机制，实现更准确、连贯的回答生成。通过联合优化策略，结合检索似然与生成交叉熵，提升模型鲁棒性与适应性。实验结果表明，所提系统在多个基准测试中优于现有检索增强与生成类方法，验证了其在生成精准、上下文相关答案方面的有效性。
原文摘要 · Abstract (English)
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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