Abstract
Recently, prompt learning has received significant attention, where the downstream tasks are reformulated to the mask-filling task with the help of a textual prompt. The key point of prompt learning is finding the most appropriate prompt. This paper proposes a novel model PromptGen, which can automatically generate prompts conditional on the input sentence. PromptGen is the first work considering dynamic prompt generation for knowledge probing, based on a pre-trained generative model. To mitigate any label information leaking from the pre-trained generative model, when given a generated prompt, we replace the query input with “None”. We pursue that this perturbed context-free prompt cannot trigger the correct label. We evaluate our model on the knowledge probing LAMA benchmark, and show that PromptGen significantly outperforms other baselines.- Anthology ID:
- 2022.findings-naacl.3
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2022
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 30–37
- Language:
- URL:
- https://aclanthology.org/2022.findings-naacl.3
- DOI:
- 10.18653/v1/2022.findings-naacl.3
- Cite (ACL):
- Yue Zhang, Hongliang Fei, Dingcheng Li, and Ping Li. 2022. PromptGen: Automatically Generate Prompts using Generative Models. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 30–37, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- PromptGen: Automatically Generate Prompts using Generative Models (Zhang et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.findings-naacl.3.pdf
- Data
- LAMA