Making a Long Story Short in Conversation Modeling

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 …

conversation cs.cl cs.hc datasets dialogue diverse domain generated gpt gpt-3 impact making metrics modeling multiple personalities quality responses story studies systems work writing