Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models

• URL: http://arxiv.org/abs/2106.13353v1
• Date: Thu, 24 Jun 2021 23:38:10 GMT
• Title: Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models
• Authors: Robert L. Logan IV, Ivana Bala\v{z}evi\'c, Eric Wallace, Fabio Petroni, Sameer Singh, Sebastian Riedel
• Abstract summary: finetuning language models (LMs) with training examples and task descriptions has been seen as critical to recent successes in few-shot learning. We show that finetuning LMs in the few-shot setting can considerably reduce the need for prompt engineering.
• Score: 48.0311578882384
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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