Drinking From a Firehose: Continual Learning With Web-Scale Natural Language.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Natural Language Processing

Background:

• Continual learning systems adapt over time through interaction.
• Evaluating continual learning algorithms at scale requires realistic benchmarks.
• Personalized online language learning (POLL) presents a significant challenge for continual learning.

Purpose of the Study:

• To introduce a large-scale benchmark for continual learning.
• To facilitate research on personalized online language learning (POLL).
• To develop and evaluate a novel continual learning algorithm.

Main Methods:

• Collected massive datasets (Firehose10 M, Firehose100 M) of 100 million tweets from one million users over six years.
• Developed a new continual learning algorithm: continual gradient descent (ConGraD).
• Evaluated ConGraD against prior methods on the Firehose datasets and existing benchmarks.

Main Results:

• ConGraD demonstrated superior performance compared to existing continual learning methods.
• The Firehose datasets provide a robust platform for web-scale continual learning evaluation.
• The study establishes a reproducible benchmark for continual learning research.

Conclusions:

• The POLL problem, Firehose datasets, and ConGraD algorithm form a comprehensive benchmark for web-scale continual learning.
• This work enables reproducible research and advances the field of continual learning.
• The findings pave the way for more adaptive and scalable AI systems.