Making Pre-trained Language Models Better Few-shot Learners

TL;DR

This paper introduces LM-BFF, a set of simple techniques for improving few-shot learning in smaller language models through prompt-based fine-tuning and dynamic demonstration selection, significantly outperforming standard methods.

Contribution

The paper presents LM-BFF, a novel, task-agnostic approach combining prompt-based fine-tuning and dynamic demonstration selection for better few-shot learning in smaller models.

Findings

Up to 30% absolute improvement over standard fine-tuning

11% average improvement across multiple NLP tasks

Effective in low-resource, minimal-resource scenarios

Abstract

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.