Making a Long Story Short in Conversation Modeling
This work addresses efficiency in conversation modeling for diverse users, though it is incremental as it builds on existing models like GPT-3.
The study investigated how varying utterance lengths affect response quality in multi-turn dialogue models, finding that in certain conversation types, utterance lengths could be reduced by up to 72% without compromising response quality.
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.