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.
@article{arxiv.2106.13353,
title = {Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models},
author = {Robert L. Logan and Ivana Balažević and Eric Wallace and Fabio Petroni and Sameer Singh and Sebastian Riedel},
journal= {arXiv preprint arXiv:2106.13353},
year = {2021}
}