Categories: AI/ML News

Over-training large language models may make them harder to fine-tune

A small team of AI researchers from Carnegie Mellon University, Stanford University, Harvard University and Princeton University, all in the U.S., has found that if large language models are over-trained, it might make them harder to fine-tune. In their paper posted on the arXiv preprint server, the group compared the impact of different amounts of training on a single LLM.
AI Generated Robotic Content

Share
Published by
AI Generated Robotic Content

Recent Posts

KREA 2: Open-Source Release

Hey everyone, We're the team behind Krea, and today we're launching Krea 2, our new…

4 hours ago

Clustering Unstructured Text with LLM Embeddings and HDBSCAN

The current era of Generative AI seems to primarily focus on chat interfaces and prompts,…

4 hours ago

Build a protein research copilot with Amazon Bedrock AgentCore

Protein researchers face a time-consuming challenge: manually searching through thousands of peptide sequences to find…

4 hours ago

Verifiable, private AI: Google Cloud expands Confidential Computing frontiers

Protecting sensitive data used with AI is a critical part of our commitment to providing…

4 hours ago

Best Dyson Deals for Prime Day: Vacuums, Hair Tools, and More

It's one of the best times to snag yourself a Dyson device, whether it's a…

5 hours ago

Brain-inspired AI architecture could computing faster and far less power-hungry

Spiking neural networks (SNNs) are artificial intelligence (AI) models inspired by how biological neurons communicate…

5 hours ago