  Prompting language models (LMs) with training examples and task descriptions
has been seen as critical to recent successes in few-shot learning. In this
work, we show that finetuning LMs in the few-shot setting can considerably
reduce the need for prompt engineering. In fact, one can use null prompts,
prompts that contain neither task-specific templates nor training examples, and
achieve competitive accuracy to manually-tuned prompts across a wide range of
tasks. While finetuning LMs does introduce new parameters for each downstream
task, we show that this memory overhead can be substantially reduced:
finetuning only the bias terms can achieve comparable or better accuracy than
standard finetuning while only updating 0.1% of the parameters. All in all, we
recommend finetuning LMs for few-shot learning as it is more accurate, robust
to different prompts, and can be made nearly as efficient as using frozen LMs.
