Black-box Prompt Learning for Pre-trained Language Models (2201.08531v3)

Abstract: The increasing scale of general-purpose Pre-trained LLMs (PLMs) necessitates the study of more efficient adaptation across different downstream tasks. In this paper, we establish a Black-box Discrete Prompt Learning (BDPL) to resonate with pragmatic interactions between the cloud infrastructure and edge devices. Particularly, instead of fine-tuning the model in the cloud, we adapt PLMs by prompt learning, which efficiently optimizes only a few parameters of the discrete prompts. Moreover, we consider the scenario that we do not have access to the parameters and gradients of the pre-trained models, except for its outputs given inputs. This black-box setting secures the cloud infrastructure from potential attack and misuse to cause a single-point failure, which is preferable to the white-box counterpart by current infrastructures. Under this black-box constraint, we apply a variance-reduced policy gradient algorithm to estimate the gradients of parameters in the categorical distribution of each discrete prompt. In light of our method, the user devices can efficiently tune their tasks by querying the PLMs bounded by a range of API calls. Our experiments on RoBERTa and GPT-3 demonstrate that the proposed algorithm achieves significant improvement on eight benchmarks in a cloud-device collaboration manner. Finally, we conduct in-depth case studies to comprehensively analyze our method in terms of various data sizes, prompt lengths, training budgets, optimization objectives, prompt transferability, and explanations of the learned prompts. Our code will be available at https://github.com/shizhediao/Black-Box-Prompt-Learning.

Black-Box Prompt Learning for Pre-trained LLMs

The research paper, "Black-Box Prompt Learning for Pre-trained LLMs," introduces a novel approach to efficiently adapt large Pre-trained LLMs (PLMs) for diverse downstream tasks through Black-box Discrete Prompt Learning (BDPL). This method is particularly relevant in contexts where the parameters and gradients of PLMs are inaccessible, limiting the adaptation processes to interactions solely with the model's outputs given specific inputs. This constraint is typical in scenarios where PLMs are accessible as APIs hosted on cloud infrastructure, such as OpenAI's GPT-3, with considerations for commercial security and cost-effective operations across cloud and edge devices.

Methodology Overview

The BDPL framework focuses on optimizing discrete prompts instead of fine-tuning PLM parameters. This approach maintains computational efficiency by minimizing the number of parameters necessary for tuning, enhancing compatibility with black-box settings that protect underlying model infrastructures from exploitation. The discrete nature of the prompts is significant for interpretability, presenting an advantage over continuous prompt methods by allowing direct implementation and understanding of learned strategies within API environments that exclusively support discrete input constructs.

The core of BDPL is its reliance on a variance-reduced policy gradient algorithm, designed to estimate gradients for the categorical distribution governing each discrete prompt token selection. In essence, the methodology distills the prompt learning task into a token selection process, optimized by gradient-free techniques due to the absence of accessible model gradients. The policy gradient method employed provides a means to refine this selection by iteratively querying PLM outputs and adjusting prompt strategies based on loss feedback derived from API predictions.

Results and Discussion

Experimental validation conducted on models such as RoBERTa and GPT-3 across various benchmarks illustrates the efficacy of BDPL in enhancing task performance under collaborative cloud-device operations. The results demonstrate notable improvements compared to other tuning methodologies within the constraints of commercial API interactions, emphasizing BDPL's utility in practical applications where model fine-tuning or gradient-based prompt optimization is infeasible.

Key observations from the experiments include:

1. Data Efficiency: BDPL operates effectively within few-shot learning paradigms, highlighting its robustness against limited training data and potential overfitting.
2. Prompt Transferability: The discrete prompt tokens exhibit viable transfer capabilities across tasks with shared linguistic structures, suggesting extended applicability in scenarios requiring multiple model deployments with minimal retraining overhead.
3. Computational Cost: By significantly reducing costs associated with querying PLMs during training, BDPL offers a scalable solution to model adaptation in resource-constrained environments.

Future Directions and Implications

The implications of BDPL extend to broader applications in AI where secure model interactions are critical, encompassing industries with stringent data protection regulations and commercial interests in model integrity. While promising, further research may explore BDPL's adaptability to multi-modal models and its integration into non-textual prediction systems.

Conclusion

This paper underscores a shift towards adaptable and secure prompt learning mechanisms in AI, heralding a direction that balances computational efficiency with interpretability within the black-box model paradigm. The BDPL framework sets a precedent for future explorations into discrete prompt optimization, providing researchers and practitioners with a viable method for enhancing model utility consistent with commercial and ethical standards.