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.

本研究针对检索增强生成方法中常忽视的外部知识库内容质量问题，提出了一种新的端到端模型E2E-AFG，该模型将答案存在性判断和文本生成整合到一个框架中。这一新颖的设计使得模型能够更加有效地关注相关内容，减少无关信息的影响，从而生成更准确的答案。实验结果表明，E2E-AFG在六个知识密集型语言数据集的评估中，在所有任务上均优于基线模型，显示出该方法的有效性和鲁棒性。

E2E-AFG：一种带有自适应过滤的端到端检索增强生成模型