Few-Shot Learning

by Synced 2023-01-24 0

Forget About Catastrophic Forgetting: Google’s Continual HyperTransformer Enables Efficient Continual Few-Shot Learning

In the new paper Continual Few-Shot Learning Using HyperTransformers, a Google Research team proposes Continual HyperTransformer, which modifies the recently published HyperTransformer few-shot learning method to sequentially update a convolutional neural network’s weights based on the information in a new task without forgetting the knowledge it learned from previous tasks.

by Synced 2022-10-12 1

AI Machine Learning & Data Science Nature Language Tech Research

‘Ask Me Anything’: Stanford U, Numbers Station & UW Madison’s Novel Prompting Strategy Enables LLMs With 30x Fewer Parameters to Outperform Few-Shot GPT3-175B

In the new paper Ask Me Anything: A Simple Strategy for Prompting Language Models, a research team from Stanford University, Numbers Station, and the University of Wisconsin-Madison presents Ask Me Anything Prompting (AMA), a simple large language model prompting strategy that enables a 30x smaller language model to outperform few-shot GPT3-175B.

by Synced 2022-09-27 0

AI Machine Learning & Data Science Research

Google’s Promptagator Creates Task-Specific Neural Retrievers From Only 8 Examples

In the new paper Promptagator: Few-shot Dense Retrieval From 8 Examples, a Google Research team proposes Prompt-based Query Generation for Retriever (Promptagator), a novel and simple approach for few-shot retrieval that leverages large language model (LLM) prompting to generate synthetic task-specific training data.

by Synced 2022-04-06 0

AI Machine Learning & Data Science Research

Google Trains a 540B Parameter Language Model With Pathways, Achieving ‘Breakthrough Performance’

A Google Research team further explores the scaling approach for improving language modelling, leveraging the new Pathways distributed ML system to train a 540 billion parameter autoregressive transformer, Pathways Language Model (PaLM), that achieves state-of-the-art few-shot performance.

by Synced 2021-11-08 2

AI Machine Learning & Data Science Nature Language Tech Research

Introducing MetaICL: A Language Model Meta-Training Framework for Few-Shot In-Context Learning

A research team from the University of Washington, Facebook AI Research and the Allen Institute for AI introduces Meta-training for InContext Learning (MetaICL), a new meta-training framework for few-shot learning where an LM is meta-trained to learn in-context — conditioning on training examples to recover the task and make predictions.

by Synced 2021-09-23 1

AI Machine Learning & Data Science Research

UMass Amherst & Google Improve Few-Shot Learning on NLP Benchmarks via Task Augmentation and Self-Training

A team from University of Massachusetts Amherst and Google Research proposes STraTA, an approach that combines task augmentation and self-training to leverage unlabelled data and improve sample efficiency and performance on NLP tasks.

by Synced 2021-07-16 3

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.

by Synced 2021-05-31 1

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.”