Papers by Michalis Korakakis
Improving Scheduled Sampling with Elastic Weight Consolidation for Neural Machine Translation (2022.findings-emnlp)
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| Challenge: | Autoregressive models trained with maximum likelihood estimation suffer from exposure bias, i.e. the discrepancy between ground-truth prefixes used during training and model-generated prefix at inference time. |
| Approach: | They propose to use Elastic Weight Consolidation to better balance mitigating exposure bias with retaining performance. |
| Outcome: | The proposed method significantly outperforms maximum likelihood estimation and scheduled sampling baselines on four translation datasets. |
ALVIN: Active Learning Via INterpolation (2024.emnlp-main)
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| Challenge: | Experimental results show that Active Learning methods ignore example groups whose prevalence may vary . supervised fine-tuning remains a critical component of model development, authors say . |
| Approach: | They propose an approach that uses interpolations to create anchors between examples . they propose to use the model to identify informative examples that counteract shortcuts . |
| Outcome: | The proposed model outperforms state-of-the-art active learning methods on six datasets . it prioritizes high-certainty instances that integrate representations from different example groups . |
Mitigating Shortcut Learning with InterpoLated Learning (2025.acl-long)
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| Challenge: | Existing shortcut mitigation approaches are model-specific, difficult to tune, computationally expensive, and fail to improve learned representations. |
| Approach: | They propose to interpolate representations of majority examples to include features from intra-class minority examples with shortcut-mitigating patterns. |
| Outcome: | The proposed method improves minority generalization over ERM and state-of-the-art mitigation methods on multiple natural language understanding tasks while preserving accuracy on majority examples. |
Improving the robustness of NLI models with minimax training (2023.acl-long)
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| Challenge: | Experimental results show that our method consistently outperforms other robustness enhancement techniques on out-of-distribution adversarial test sets, while maintaining high in-distance accuracy. |
| Approach: | They propose a minimax objective between a learner model being trained for the task and an auxiliary model aiming to maximize the learner's loss by up-weighting underrepresented "hard" examples with patterns that contradict the shortcuts learned from the prevailing "easy" examples. |
| Outcome: | The proposed method outperforms other robustness enhancement techniques on out-of-distribution adversarial test sets while maintaining high in-distance accuracy. |