Categories: AI/ML News

How MIT is training AI language models in an era of quality data scarcity

As more sophisticated AI language models are developed, more quality data is needed for training them. MIT is working to solve this issue.Read More

Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling

This paper has been accepted at the Data Problems for Foundation Models workshop at ICLR 2024. Large language models are trained on massive scrapes of the web, which are often unstructured, noisy, and poorly phrased. Current scaling laws show that learning from such data requires an abundance of both compute…

May 8, 2024

In "FAANG"

Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling

Large language models are trained on massive scrapes of the web, which are often unstructured, noisy, and poorly phrased. Current scaling laws show that learning from such data requires an abundance of both compute and data, which grows with the size of the model being trained. This is infeasible both…

August 7, 2024

In "FAANG"

How to Speed-Up Training of Language Models

This article is divided into four parts; they are: • Optimizers for Training Language Models • Learning Rate Schedulers • Sequence Length Scheduling • Other Techniques to Help Training Deep Learning Models Adam has been the most popular optimizer for training deep learning models.

December 1, 2025

In "AI/ML Research"

AI Generated Robotic Content