Part of International Conference on Representation Learning 2025 (ICLR 2025) Conference
Peiqi Wang, Barbara Lam, Yingcheng Liu, Ameneh Asgari-Targhi, Rameswar Panda, William Wells III, Tina Kapur, Polina Golland
We present a novel approach to calibrating linguistic expressions of certainty, e.g., "Maybe" and "Likely". Unlike prior work that assigns a single score to each certainty phrase, we model uncertainty as distributions over the simplex to capture their semantics more accurately. To accommodate this new representation of certainty, we generalize existing measures of miscalibration and introduce a novel post-hoc calibration method. Leveraging these tools, we analyze the calibration of both humans (e.g., radiologists) and computational models (e.g., language models) and provide interpretable suggestions to improve their calibration.