Papers with manual selection
Label-Aware Automatic Verbalizer for Few-Shot Text Classification in Mid-To-Low Resource Languages (2024.acl-srw)
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| Challenge: | Prompt-based learning has shown its effectiveness in few-shot text classification. |
| Approach: | They propose a prompt-based learning verbalizer that automatically selects a word to represent each class . they use the label name along with the conjunction "and" to induce the model to generate more effective words for the verbaliser. |
| Outcome: | The proposed method outperforms existing verbalizers on four Southeast Asian languages. |
UOR: Universal Backdoor Attacks on Pre-trained Language Models (2024.findings-acl)
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| Challenge: | Existing methods to attack pre-trained language models rely on manual selection of triggers and backdoor representations. |
| Approach: | They propose a backdoor attack method that turns manual selection into automatic optimization . they propose to use poisoned contrastive learning to learn more uniform backdoor representations . |
| Outcome: | The proposed method achieves better attack performance on text classification tasks compared to manual methods. |
MoR: Better Handling Diverse Queries with a Mixture of Sparse, Dense, and Human Retrievers (2025.emnlp-main)
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| Challenge: | Different retrievers offer distinct, often complementary signals, but they are not optimal for all queries. |
| Approach: | They propose a zero-shot, weighted combination of heterogeneous retrievers . they validate this intuition by incorporating specialized non-oracle human information sources . |
| Outcome: | Experiments show that a mixture of heterogeneous retrievers outperforms each retriever and larger models by +10.8% and +3.9% on average. |