对话建模中的长话短说
Making a Long Story Short in Conversation Modeling
AI总结：
本研究聚焦多轮对话建模，以GPT-3为基础模型结合多数据集与指标，探究话语长度对回复质量的影响，发现部分对话中话语最多可缩减72%而不明显降低后续回复质量。
摘要：
对话系统需要适配具有独特个性与不同写作风格的各类用户。本研究在多轮对话建模领域，探究了不同话语长度对对话模型生成后续回复质量的影响。我们以GPT-3为基础模型，使用多个对话数据集与多项评估指标，对对话模型的这一维度展开了全面探究。我们的分析揭示了话语长度与对话系统生成的后续回复质量之间的复杂关联。实证结果表明，对于某些类型的对话，话语长度最多可缩减72%，而后续回复的质量不会出现可察觉的差异。
英文摘要：
Conversation systems accommodate diverse users with unique personalities and distinct writing styles. Within the domain of multi-turn dialogue modeling, this work studies the impact of varied utterance lengths on the quality of subsequent responses generated by conversation models. Using GPT-3 as the base model, multiple dialogue datasets, and several metrics, we conduct a thorough exploration of this aspect of conversational models. Our analysis sheds light on the complex relationship between utterance lengths and the quality of follow-up responses generated by dialogue systems. Empirical findings suggests that, for certain types of conversations, utterance lengths can be reduced by up to 72% without any noticeable difference in the quality of follow-up responses.
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。
