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Derivative-free prompt learning has emerged as a lightweight alternative toprompt tuning, which only requires model inference to optimize the prompts.However, existing work did not take full advantage of the over-parameterizedcharacteristics of large pre-trained language models (PLMs). In this paper, wepropose Clip-Tuning, a simple yet effective method that adopts diverse frozen"thinned" networks of PLMs to obtain a mixture of rewards and thus advance thederivative-free prompt learning. The thinned networks consist of all the hiddenunits that survive a stationary dropout strategy, whose inference predictionsreflect an ensemble of partial views over prompted training samples. Our methodoutperforms previous gradient-free prompt learning methods and achieves paritywith gradient-based counterparts on seven language understanding benchmarksunder few-shot settings.

Clip-Tuning: Towards Derivative-free Prompt Learning with a Mixture of Rewards