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        <title>PromptGen: Automatically Generate Prompts using Generative Models</title>
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        <namePart type="given">Yue</namePart>
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        <namePart type="given">Hongliang</namePart>
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    <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.</abstract>
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        <date>2022-07</date>
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%0 Conference Proceedings
%T PromptGen: Automatically Generate Prompts using Generative Models
%A Zhang, Yue
%A Fei, Hongliang
%A Li, Dingcheng
%A Li, Ping
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F zhang-etal-2022-promptgen
%X 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.
%R 10.18653/v1/2022.findings-naacl.3
%U https://aclanthology.org/2022.findings-naacl.3
%U https://doi.org/10.18653/v1/2022.findings-naacl.3
%P 30-37

Markdown (Informal)

[PromptGen: Automatically Generate Prompts using Generative Models](https://aclanthology.org/2022.findings-naacl.3) (Zhang et al., Findings 2022)

• PromptGen: Automatically Generate Prompts using Generative Models (Zhang et al., Findings 2022)

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.