Knowledge Grounded Dialogue Generation

Knowledge-grounded dialogue generation (KGD) aims to create conversational agents that produce factually accurate and engaging responses by integrating external knowledge sources. Current research focuses on mitigating issues like hallucinations (factual errors) and improving response diversity, employing techniques such as reinforcement learning with carefully designed reward functions, novel decoding methods that balance factuality and creativity, and architectures that leverage both internal model knowledge and external knowledge graphs or retrieved text. Advances in KGD are significant for building more trustworthy and informative conversational AI systems, with applications ranging from virtual assistants to educational tools.