Papers with universal evaluation
ConEntail: An Entailment-based Framework for Universal Zero and Few Shot Classification with Supervised Contrastive Pretraining (2023.eacl-main)
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| Challenge: | Existing models for text classification are not universally applicable and lack annotated data. |
| Approach: | They propose a framework for universal zero and few shot classification with supervised contrastive pretraining that can generalize to diverse classification tasks in both zero and many shot settings. |
| Outcome: | The proposed framework outperforms baseline models in zero and few shot settings. |