Making a Long Story Short in Conversation Modeling
Yufei Tao Tiernan Mines Ameeta Agrawal
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.