Black-box Prompt Tuning with Subspace Learning
Black-box prompt tuning uses derivative-free optimization algorithms to learn prompts in low-dimensional subspaces instead of back-propagating through the network of Large Language Models (LLMs). Recent studies have found that black-box prompt tuning lacks versatility across tasks and LLMs, which we believe is related to the inappropriate choice of subspaces. In this paper, we propose Black-box prompt tuning with Subspace Learning (BSL) to improve the versatility of black-box prompt tuning. Based on the assumption that nearly optimal prompts for similar tasks exist in a common subspace, we propose identifying such subspaces by meta-learning on a set of similar source tasks. Therefore, for a target task that shares similarities with source tasks, we guarantee that optimizing in the subspace can find a prompt that performs well on the target task. Experiments confirm that our BSL framework consistently achieves competitive performance regardless of downstream tasks and LLMs.
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