PromptGen: Automatically Generate Prompts using Generative Models
Yue Zhang, Hongliang Fei, Dingcheng Li, Ping Li · Findings of the Association for Computational Linguistics: NAACL 2022 · 2022
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.