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. |
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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. |
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Zhaofeng Wu, Robert L Logan IV, Pete Walsh, Akshita Bhagia, Dirk Groeneveld, Sameer Singh, Iz Beltagy
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| Challenge: | Prompt tuning for pre-trained language models has shown remarkable performance . however, prompt tuning is still not fully explored . |
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