Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915350725001216 |
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| author | Huang, Xinyue Lin, Ziqi Sun, Fang Zhang, Wenchao Tong, Kejian Liu, Yunbo |
| author_facet | Huang, Xinyue Lin, Ziqi Sun, Fang Zhang, Wenchao Tong, Kejian Liu, Yunbo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_16037 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3 Huang, Xinyue Lin, Ziqi Sun, Fang Zhang, Wenchao Tong, Kejian Liu, Yunbo Computation and Language Machine Learning 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. |
| title | Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3 |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2506.16037 |