Tag: Few-Shot Learning

AI Machine Learning & Data Science Research

Baidu’s Knowledge-Enhanced ERNIE 3.0 Pretraining Framework Delivers SOTA NLP Results, Surpasses Human Performance on the SuperGLUE Benchmark

A research team from Baidu proposes ERNIE 3.0, a unified framework for pretraining large-scale, knowledge-enhanced models that can easily be tailored for both natural language understanding and generation tasks with zero-shot learning, few-shot learning or fine-tuning, and achieves state-of-the-art results on NLP tasks.

AI Machine Learning & Data Science Research

NYU, Facebook & CIFAR Present ‘True Few-Shot Learning’ for Language Models Whose Few-Shot Ability They Say Is Overestimated

A research team from New York University, Facebook AI, and a CIFAR Fellow in Learning in Machines & Brains raise doubts regarding large-scale pretrained language models’ few-shot learning abilities. The researchers re-evaluate such abilities with held-out examples unavailable, which they propose constitutes “true few-shot learning.”