Papers by Zhihang Tan
Surprise Calibration for Better In-Context Learning (2025.emnlp-main)
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| Challenge: | Existing methods for in-context learning apply fixed class priors across all inputs . existing methods rely on retraining and retrain models . |
| Approach: | They propose a Bayesian-based method to capture the temporal dynamics of class priors . they identify "surprise" as an informative signal for class prior shift . |
| Outcome: | The proposed method outperforms existing methods on a range of benchmark tasks. |