Evaluating Theory of (an uncertain) Mind: Predicting the Uncertain Beliefs of Others from Conversational Cues

Typically, when evaluating Theory of Mind, we consider the beliefs of others to be binary: held or not held. But what if someone is unsure about their own beliefs? How can we quantify this uncertainty? We propose a new suite of tasks, challenging language models (LMs) to model the uncertainty of participants in a dialogue. We design these tasks around conversation forecasting, where the goal is to predict the probability of an unobserved conversation outcome. Uniquely, we view conversation agents themselves as forecasters, asking an LM to predict the uncertainty of an individual from their language use. We experiment with scaling methods, bagging, and demographic context for this regression task, conducting experiments on three dialogue corpora (social, negotiation, task-oriented) with eight LMs. While LMs can explain up to 7% variance in the uncertainty of others, we highlight the difficulty of the tasks and room for future work, especially in tasks that require explicit shifts in perspective. How certain is S1 they are more satisfied than would occur by chance? → Ground-truth P = 11% → Predicted P = 40% How certain is S2 they like S1 more than would occur by chance? → Ground-truth P = 27% → Predicted P = 90% How certain is S2 they are more satisfied than would occur by chance? → Ground-truth P = 81% → Predicted P = 90%