Mind2Dialogue Framework Trains Language Models to Model User Mental States
A new arXiv paper introduces Mind2Dialogue, a method that trains language models to be more aware of the people they interact with by simulating users' mental states during training. The authors frame the problem as a supervision gap: models need signals about human internal states, which are rarely available in standard dialogue data. The approach targets long-term collaboration in learning, reasoning, and decision-making tasks.