zero-shot learning

by Synced 2022-05-30 3

Tokyo U & Google Brain Train Large Language Models as Zero-Shot Reasoners

In the new paper Large Language Models are Zero-Shot Reasoners, a research team from the University of Tokyo and Google Brain demonstrates that large language models (LLMs) can become good zero-shot reasoners through the addition of a simple prompt — “Let’s think step by step” — that elicits a step-by-step thinking process before each question is answered. Their Zero-shot-CoT model achieves huge performance gains compared to the zero-shot baseline.

by Synced 2022-04-12 1

AI Machine Learning & Data Science Research

Google Builds Language Models with Socratic Dialogue to Improve Zero-Shot Multimodal Reasoning Capabilities

In the new paper Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language, Google researchers argue that the diversity of different foundation models is symbiotic and that it is possible to build a framework that uses structured Socratic dialogue between pre-existing foundation models to formulate new multimodal tasks as a guided exchange between the models without additional finetuning.

by Synced 2021-10-21 0

AI Machine Learning & Data Science Research

DeepMind’s Fictitious Co-Play Trains RL Agents to Collaborate with Novel Humans Without Using Human Data

A DeepMind research team explores the problem of how to train agents to collaborate well with novel human partners without using human data and presents Fictitious Co-Play (FCP), a surprisingly simple approach designed to address this challenge.

by Synced 2021-10-07 0

AI Machine Learning & Data Science Research

Facebook & CMU’s Zero-Shot VideoCLIP Outperforms Fully-Supervised SOTA Methods for Video-Text Understanding

A research team from Facebook AI and Carnegie Mellon University presents VideoCLIP, a contrastive approach for pretraining a unified model for zero-shot video and text understanding without requiring annotated data for downstream tasks.

by Synced 2021-09-24 1

AI Machine Learning & Data Science Nature Language Tech Research

Google’s Zero-Label Language Learning Achieves Results Competitive With Supervised Learning

A Google AI research team explores zero-label learning (training with synthetic data only) in natural language processing, and introduces Unsupervised Data Generation (UDG), a training data creation procedure designed to synthesize high-quality training data without human annotations.