  Extremely large pre-trained language models (PTMs) such as GPT-3 are usually
released as a service. It allows users to design task-specific prompts to query
the PTMs through some black-box APIs. In such a scenario, which we call
Language-Model-as-a-Service (LMaaS), the gradients of PTMs are usually
unavailable. Can we optimize the task prompts by only accessing the model
inference APIs? This paper proposes the black-box tuning framework to optimize
the continuous prompt prepended to the input text via derivative-free
optimization. Instead of optimizing in the original high-dimensional prompt
space, which is intractable for traditional derivative-free optimization, we
perform optimization in a randomly generated subspace due to the low intrinsic
dimensionality of large PTMs. The experimental results show that the black-box
tuning with RoBERTa on a few labeled samples not only significantly outperforms
manual prompt and GPT-3's in-context learning, but also surpasses the
gradient-based counterparts, i.e., prompt tuning and full model tuning.
