Conversational Feedback

Conversational feedback research focuses on improving human-computer interaction by analyzing and leveraging user responses within dialogues. Current efforts concentrate on developing models that effectively utilize various feedback types, including explicit ratings, implicit cues like backchannels, and relative comparisons, often within frameworks like dueling bandits and generalized linear models. This research is crucial for enhancing the performance of conversational AI systems across diverse applications, such as recommendation systems and emotional support chatbots, by enabling more natural and effective interactions. The development of large-scale datasets specifically designed for evaluating feedback integration and refinement further accelerates progress in this field.