The recent GPT-3 modelachieves 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—betterfew-shotfine-tuning oflanguagemodels111Alternatively, language models’bestfriendsforever.—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.222Our implementation is publicly available athttps://github.com/princeton-nlp/LM-BFF.