Papers with zero-shot learner
Prompt Consistency for Zero-Shot Task Generalization (2022.findings-emnlp)
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| Challenge: | Recent work has shown that pre-trained language models can perform zero-shot generalization to new tasks without annotated examples. |
| Approach: | They propose to regularize prompt consistency to encourage consistent predictions over a diverse set of prompts. |
| Outcome: | The proposed approach outperforms the state-of-the-art zero-shot learner, T0, on 9 out of 11 datasets across 4 NLP tasks by 10.6 absolute points in terms of accuracy. |
Prompt to be Consistent is Better than Self-Consistent? Few-Shot and Zero-Shot Fact Verification with Pre-trained Language Models (2023.findings-acl)
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| Challenge: | Existing methods for few-shot and zero-shot fact verification require a large set of training data. |
| Approach: | They propose a method to prompt pre-trained language models to be consistent to improve the factuality assessment capability of PLMs. |
| Outcome: | The proposed method outperforms state-of-the-art few-shot fact verification models with a small number of unlabeled instances on zero-shot verification. |