Is This Just Fantasy? Language Model Representations Reflect Human Judgments of Event Plausibility
This research found that language models learn linear representations called 'modal difference vectors' to categorize sentences based on possibility (e.g., possible, impossible, inconceivable). These vectors are more reliable than simple probability estimates and reflect how humans actually categorize events. The study shows these vectors correlate with human judgments about event likelihood and features like imageability.