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Few-shot learning allows pre-trained language models to adapt to downstreamtasks while using a limited number of training examples. However, practicalapplications are limited when all model parameters must be optimized. In thiswork we apply a new technique for parameter efficient few shot learning whileadopting a strict definition of parameter efficiency. Our training methodcombines 1) intermediate training by reformulating natural language tasks asentailment tasks \cite{wang_entailment_2021} and 2) differentiable optimizationof template and label tokens \cite{zhang_differentiable_2021}. We quantify thetradeoff between parameter efficiency and performance in the few-shot regimeand propose a simple model agnostic approach that can be extended to any taskBy achieving competitive performance while only optimizing 3\% of a model'sparameters and allowing for batched inference, we allow for more efficientpractical deployment of models.

Differentiable Entailment for Parameter Efficient Few Shot Learning