BriefGPT.xyz
May, 2022
基于提示的少样本语言学习的对比学习
Contrastive Learning for Prompt-Based Few-Shot Language Learners
HTML
PDF
Yiren Jian, Chongyang Gao, Soroush Vosoughi
TL;DR
本文提出一种基于对比学习的框架,使用不同的增强“视图”将同一类别的输入聚类,远离来自不同类别的输入,将对比损失与标准的掩码语言建模(MLM)损失相结合,并应用于基于提示的少样本学习者,实验结果表明,我们的方法在15种不同的语言任务中表现优于现有的先进方法。
Abstract
The impressive performance of
gpt-3
using natural language prompts and in-context learning has inspired work on better fine-tuning of moderately-sized models under this paradigm. Following this line of work, we present a
→