# Black-box Prompt Learning for Pre-trained Language Models

**Authors:** Shizhe Diao, Xuechun Li, Yong Lin, Zhichao Huang, Tong Zhang  
**arXiv:** [2201.08531](https://arxiv.org/abs/2201.08531)  
**Date:** 2022-01-21  
**Summary page:** https://www.summarizepaper.com/en/paper/2201.08531v1/

## Key points

- The paper explores domain-specific fine-tuning strategies for large pre-trained models
- Introduces a new scenario called black-box fine-tuning where access to the pre-trained model is limited to its outputs given inputs
- Proposes a solution called black-box prompt which belongs to the prompt learning family
- Leverages the knowledge learned by pre-trained models from the pre-training corpus
- Achieves state of the art performance on eight datasets
- Analyzes different human designed objectives and prompt lengths
- Provides intuitive explanations to showcase the robustness and flexibility of their approach
- Presents a novel approach for black box fine tuning of pre trained language models using black box prompts
- Experimental results highlight its effectiveness in various scenarios and its ability to outperform existing methods on multiple datasets

## Summary

The paper titled "Black-box Prompt Learning for Pre-trained Language Models" by Shizhe Diao, Xuechun Li, Yong Lin, Zhichao Huang, and Tong Zhang explores domain-specific fine-tuning strategies for large pre-trained models. While previous studies focused on white-box settings where model architectures and parameters are tunable or visible, this work introduces a new scenario called black-box fine-tuning. In this scenario, access to the pre-trained model is limited to its outputs given inputs. To address this challenge, the authors propose a solution called black-box prompt which belongs to the prompt learning family. This technique leverages the knowledge learned by pre-trained models from the pre-training corpus. Experimental results demonstrate that the proposed method achieves state of the art performance on eight datasets. The authors further analyze different human designed objectives, prompt lengths and provide intuitive explanations to showcase the robustness and flexibility of their approach. Overall, this paper presents a novel approach for black box fine tuning of pre trained language models using black box prompts. The experimental results highlight its effectiveness in various scenarios and its ability to outperform existing methods on multiple datasets.