Information Seeking Dialog

Information-seeking dialog research focuses on building systems that can engage in natural, informative conversations to answer user questions, often grounding responses in external documents. Current research heavily utilizes large language models (LLMs) to generate synthetic training data, employing techniques like self-instruction and semi-automatic annotation to overcome the limitations of manually creating large conversational datasets. This work is significant because it addresses the scarcity of high-quality training data, a major bottleneck in developing robust and effective conversational AI systems for information retrieval and question answering. The resulting improvements in model performance have implications for various applications, including customer service chatbots and educational tools.