Papers by Jingrui Hou
Surprise Calibration for Better In-Context Learning (2025.emnlp-main)
Copied to clipboard
| 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. |
Enhancing Ancient Chinese Understanding with Derived Noisy Syntax Trees (2023.acl-srw)
Copied to clipboard
| Challenge: | Syntactic information is not used in modern Chinese understanding tasks due to the lack of syntactical annotation. |
| Approach: | They propose a confidence-based syntax encoding network to alleviate the side effects of unsupervised syntax derivation and the incompatibility between ancient and modern Chinese. |
| Outcome: | The proposed component alleviates side effects from unsupervised syntax derivation and incompatibility between ancient and modern Chinese. |