一种用于知识驱动对话生成的事实性与多样性协调解码方法
A Factuality and Diversity Reconciled Decoding Method for Knowledge-Grounded Dialogue Generation
AI总结：
针对知识驱动对话生成中事实性与多样性难以兼顾的问题，提出DoGe方法，基于模型事实置信度动态切换内外知识使用，在三个数据集上实现多样性提升且保持事实性，显著优于现有解码基线。
摘要：
引入外部知识可增强对话生成中回复的事实性，但过度强调知识可能导致回复缺乏吸引力和多样性。现有方法通过在采样中引入随机性提升多样性，然而此类采样方法会损害对话生成的事实性。本研究为在不依赖存疑随机性的前提下提升创造性，并在源知识驱动范式内巧妙协调事实性与多样性，提出一种名为DoGe的新方法。DoGe能基于模型的事实置信度，动态切换内部参数知识与外部源知识的使用。在三个广泛使用的数据集上进行的大量实验表明，DoGe不仅能增强回复多样性，还能保持事实性，且显著优于其他多种解码策略基线。
英文摘要：
Grounding external knowledge can enhance the factuality of responses in dialogue generation. However, excessive emphasis on it might result in the lack of engaging and diverse expressions. Through the introduction of randomness in sampling, current approaches can increase the diversity. Nevertheless, such sampling method could undermine the factuality in dialogue generation. In this study, to discover a solution for advancing creativity without relying on questionable randomness and to subtly reconcile the factuality and diversity within the source-grounded paradigm, a novel method named DoGe is proposed. DoGe can dynamically alternate between the utilization of internal parameter knowledge and external source knowledge based on the model's factual confidence. Extensive experiments on three widely-used datasets show that DoGe can not only enhance response diversity but also maintain factuality, and it significantly surpasses other various decoding strategy baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
