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arXiv · 2402.00143

Making a Long Story Short in Conversation Modeling

Yufei Tao·Tiernan Mines·Ameeta Agrawal
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Abstract

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.

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Yufei Tao, Tiernan Mines, Ameeta Agrawal. 2024-01-31. Making a Long Story Short in Conversation Modeling. https://arxiv.org/abs/2402.00143

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