Papers with explicit learning
TTM-RE: Memory-Augmented Document-Level Relation Extraction (2024.acl-long)
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| Challenge: | Existing methods for document-level relation extraction are ineffective in exploiting the full potential of large amounts of training data with varied noise levels. |
| Approach: | They propose a novel approach that integrates a trainable memory module with a noisy-robust loss function that accounts for the positive-unlabeled setting to unlock the full potential of large-scale noisy training data. |
| Outcome: | The proposed model outperforms existing methods on a ReDocRED benchmark dataset with an absolute F1 score improvement of over 3%. |
Explicit Learning and the LLM in Machine Translation (2025.emnlp-main)
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| Challenge: | a growing number of researchers are examining whether large language models can learn to translate a "new" language using grammar books. |
| Approach: | They examine an LLM's ability to learn new languages using grammar books . authors suggest alternative fine-tuning strategies to improve explicit learning . |
| Outcome: | The proposed model can learn low-resource languages described in grammar books but lacking extensive corpora. |