  The recent GPT-3 model (Brown et al., 2020) achieves remarkable few-shot
performance solely by leveraging a natural-language prompt and a few task
demonstrations as input context. Inspired by their findings, we study few-shot
learning in a more practical scenario, where we use smaller language models for
which fine-tuning is computationally efficient. We present LM-BFF--better
few-shot fine-tuning of language models--a suite of simple and complementary
techniques for fine-tuning language models on a small number of annotated
examples. Our approach includes (1) prompt-based fine-tuning together with a
novel pipeline for automating prompt generation; and (2) a refined strategy for
dynamically and selectively incorporating demonstrations into each context.
Finally, we present a systematic evaluation for analyzing few-shot performance
on a range of NLP tasks, including classification and regression. Our
experiments demonstrate that our methods combine to dramatically outperform
standard fine-tuning procedures in this low resource setting, achieving up to
30% absolute improvement, and 11% on average across all tasks. Our approach
makes minimal assumptions on task resources and domain expertise, and hence
constitutes a strong task-agnostic method for few-shot learning.
