Incorporating external knowledge into dialogue generation (KIDG) is crucial
for improving the correctness of response, where evidence fragments serve as
knowledgeable snippets supporting the factual dialogue replies. However,
introducing irrelevant content often adversely impacts reply quality and easily
leads to hallucinated responses. Prior work on evidence retrieval and
integration in dialogue systems falls short of fully leveraging existing
evidence since the model fails to locate useful fragments accurately and
overlooks hidden evidence labels within the KIDG dataset. To fully Unleash the
potential of evidence, we propose a framework to effectively incorporate
Evidence in knowledge-Intensive Dialogue Generation (u-EIDG). Specifically, we
introduce an automatic evidence generation framework that harnesses the power
of Large Language Models (LLMs) to mine reliable evidence veracity labels from
unlabeled data. By utilizing these evidence labels, we train a reliable
evidence indicator to effectively identify relevant evidence from retrieved
passages. Furthermore, we propose an evidence-augmented generator with an
evidence-focused attention mechanism, which allows the model to concentrate on
evidenced segments. Experimental results on MultiDoc2Dial demonstrate the
efficacy of evidential label augmentation and refined attention mechanisms in
improving model performance. Further analysis confirms that the proposed method
outperforms other baselines (+3~+5 points) regarding coherence and factual
consistency.

整合外部知识进入对话生成中的关键性为提高回应的正确性，在这里，证据片段作为有根据的支持性知识碎片。本文提出一种有效地整合证据到知识密集型对话生成中的框架，通过引入自动证据生成框架和证据聚焦的注意机制，扩大了证据的潜力。实验结果表明，提出的方法在模型性能方面优于其他基准（+3~+5 分），在连贯性和事实一致性方面有进一步的分析证实。