Black-box Prompt Tuning with Subspace Learning
This work addresses a domain-specific problem for users of black-box prompt tuning in LLMs, offering an incremental improvement.
The paper tackles the lack of versatility in black-box prompt tuning across tasks and LLMs by introducing BSL, which uses meta-learning to identify common subspaces for similar tasks, resulting in competitive performance on various downstream tasks and LLMs.
Black-box prompt tuning employs derivative-free optimization algorithms to learn prompts within low-dimensional subspaces rather than back-propagating through the network of Large Language Models (LLMs). Recent studies reveal that black-box prompt tuning lacks versatility across tasks and LLMs, which we believe is related to the suboptimal choice of subspaces. In this paper, we introduce Black-box prompt tuning with Subspace Learning (BSL) to enhance the versatility of black-box prompt tuning. Based on the assumption that nearly optimal prompts for similar tasks reside in a common subspace, we propose identifying such subspaces through meta-learning on a collection of similar source tasks. Consequently, for a target task that shares similarities with the source tasks, we expect that optimizing within the identified subspace can yield a prompt that performs well on the target task. Experimental results confirm that our BSL framework consistently achieves competitive performance across various downstream tasks and LLMs.