Making Pre trained Language Models Better Few shot Learners
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 models1 a suite of simple and complementary techniques for finetuning 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.2
ACL/IJCNLP