Response Generation Model

Response generation models aim to create natural and relevant text responses in various conversational contexts, focusing on improving the quality, empathy, and fairness of generated outputs. Current research emphasizes addressing challenges in multi-party conversations, handling long-term interactions and persona management, and mitigating biases inherent in pre-trained language models, often employing techniques like reinforcement learning, graph neural networks, and causal discovery. These advancements hold significant implications for improving human-computer interaction in diverse applications, including chatbots, virtual assistants, and assistive technologies.