自适应过滤提升检索生成质量，让大模型更准地回答问题。
E2E-AFG: An End-to-End Model with Adaptive Filtering for Retrieval-Augmented Generation
- 端到端融合答案存在性判断与生成，动态筛选相关文本。
- 在6个知识密集型数据集上均超越基线模型，表现稳定可靠。
- 适合需要高精度问答的场景，如智能客服、医疗咨询。
检索增强生成方法常忽略外部知识库中检索内容的质量，导致无关信息或潜在错误信息影响大语言模型的生成效果。本文提出一种端到端自适应过滤的检索增强生成模型（E2E-AFG），将答案存在性判断与文本生成整合至单一端到端框架中。该设计使模型能更有效地聚焦于相关文本，降低无关信息干扰，生成更准确的答案。我们在六个代表性知识密集型语言数据集上评估E2E-AFG，结果表明其在所有任务上均持续优于基线模型，验证了该方法的有效性与鲁棒性。
原文摘要 · Abstract (English)
Retrieval-augmented generation methods often neglect the quality of content retrieved from external knowledge bases, resulting in irrelevant information or potential misinformation that negatively affects the generation results of large language models. In this paper, we propose an end-to-end model with adaptive filtering for retrieval-augmented generation (E2E-AFG), which integrates answer existence judgment and text generation into a single end-to-end framework. This enables the model to focus more effectively on relevant content while reducing the influence of irrelevant information and generating accurate answers. We evaluate E2E-AFG on six representative knowledge-intensive language datasets, and the results show that it consistently outperforms baseline models across all tasks, demonstrating the effectiveness and robustness of the proposed approach.
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