Dialogue Data

Dialogue data research focuses on creating and analyzing datasets of conversational interactions to improve the capabilities of dialogue systems. Current research emphasizes developing methods for generating high-quality, diverse dialogue data, often leveraging large language models (LLMs) and techniques like data augmentation and in-context learning, to address data scarcity issues across various languages and domains. This work is crucial for advancing the development of more natural, engaging, and task-oriented dialogue systems with applications in education, mental health, customer service, and other fields. Furthermore, research is actively exploring improved evaluation metrics for dialogue quality and faithfulness, moving beyond simple metrics to more nuanced assessments aligned with human judgment.