One-Shot Calibration via Model Editing

Model editing aims to correct errors in large, pretrained models by replacing model generations with user-provided target responses. In practice, however, user feedback often contains multiple plausible answers with varying uncertainty, forming a natural target distribution to not only correct but also calibrate model generation. Existing editors ignore this uncertainty and require a single target answer for each edit. We therefore propose SoftEdit, the first model editor to leverage uncertainty in user responses and perform one-shot calibration during editing. To generalize calibration to unseen samples, SoftEdit uses co-plausibility relationships between answers while preserving real distributional shifts, addressing the new distributional generality-locality tradeoff. Across three language models, three vision-language models, and two large datasets, SoftEdit achieves state-of-the-art performance, ultimately showing that uncertainty in user responses enables generalizable calibration of pretrained models beyond single-answer corrections.