CAPE: A Chinese Dataset for Appraisal-based Emotional Generation using Large Language Models
This work addresses the problem of improving emotional expression in conversational agents for more nuanced human-computer interactions, though it is incremental as it builds on existing appraisal theory and dataset creation methods.
The authors tackled the challenge of generating emotionally appropriate responses in conversational AI by creating CAPE, a Chinese dataset based on cognitive appraisal theory, and demonstrated that models trained on it produce responses more aligned with human emotional expressions.
Generating emotionally appropriate responses in conversations with large language models presents a significant challenge due to the complexities of human emotions and cognitive processes, which remain largely underexplored in their critical role in social interactions. In this study, we introduce a two-stage automatic data generation framework to create CAPE, a Chinese dataset named Cognitive Appraisal theory-based Emotional corpus. This corpus facilitates the generation of dialogues with contextually appropriate emotional responses by accounting for diverse personal and situational factors. We propose two tasks utilizing this dataset: emotion prediction and next utterance prediction. Both automated and human evaluations demonstrate that agents trained on our dataset can deliver responses that are more aligned with human emotional expressions. Our study shows the potential for advancing emotional expression in conversational agents, paving the way for more nuanced and meaningful human-computer interactions.