Collaborative Discrete-Continuous Black-Box Prompt Learning for Language Models

Hualin Zhang, Haozhen Zhang, Zhekai Liu, Bin Gu, Yi Chang

Keywords: Black-box prompt learning, discrete optimization

Abstract:

Large Scale Pre-Trained Language Models (PTMs) have demonstrated unprecedented capabilities across diverse natural language processing tasks.
Adapting such models to downstream tasks is computationally intensive and time-consuming, particularly in black-box scenarios common in Language-Model-as-a-Service (LMaaS) environments, where model parameters and gradients are inaccessible. Recently, black-box prompt learning using zeroth-order gradients has emerged as a promising approach to address these challenges by optimizing learnable continuous prompts in embedding spaces, starting with \textit{randomly initialized discrete text prompts}. However, its reliance on randomly initialized discrete prompts limits adaptability to diverse downstream tasks or models. To address this limitation,
this paper introduces ZO-PoG, a novel framework that optimizes prompts through a collaborative approach, combining Policy Gradient optimization for initial discrete text prompts and Zeroth-Order optimization for continuous prompts in embedding space. By optimizing collaboratively between discrete and continuous prompts, ZO-PoG maximizes adaptability to downstream tasks, achieving superior results without direct access to the model’s internal structures.
Importantly, we establish the sub-linear convergence of ZO-PoG under mild assumptions.
The experiments on different datasets demonstrate significant improvements in various tasks compared to the baselines.

Primary Area: optimization

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Submission Number: 6595