Papers with self-ensembling
Uncertainty-Aware Bootstrap Learning for Joint Extraction on Distantly-Supervised Data (2023.acl-short)
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| Challenge: | Existing approaches to extract entity pairs and their relations from labeled data are noisy and expensive. |
| Approach: | They propose a bootstrap learning approach that is motivated by intuition that the higher the uncertainty of an instance, the more likely the model confidence is inconsistent with the ground truths. |
| Outcome: | The proposed method outperforms baselines and related methods on two large datasets. |
Large Language Models are Miscalibrated In-Context Learners (2025.findings-acl)
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| Challenge: | In-context Learning and Supervised Fine-Tuning have emerged as pre-dominant methodologies for machine learning and NLP. |
| Approach: | They propose to use self-ensembling to improve both performance and calibration of language models. |
| Outcome: | The proposed learning paradigms can achieve better calibration and better performance than the previous learning paradigm. |