VentureBeat

Forget data labeling: Tencent’s R-Zero shows how LLMs can train themselves

A new training framework developed by researchers at Tencent AI Lab and Washington University in St. Louis enables large language models (LLMs) to improve themselves without requiring any ...
VentureBeat

Researchers find you don’t need a ton of data to train LLMs for reasoning tasks

Large language models (LLMs) can learn complex reasoning tasks without relying on large datasets, according to a new study by researchers at Shanghai Jiao Tong University. Their findings show that ...
TechRepublic

Cisco Talos Researcher Reveals Method That Causes LLMs to Expose Training Data

Cisco Talos Researcher Reveals Method That Causes LLMs to Expose Training Data Your email has been sent In this TechRepublic interview, Cisco researcher Amy Chang details the decomposition method and ...
SiliconANGLE

OpenAI finds DeepSeek used its data to train R1 reasoning model

OpenAI believes its data was used to train DeepSeek’s R1 large language model, multiple publications reported today. DeepSeek is a Chinese artificial intelligence provider that develops open-source ...
Cult of Mac

How Apple gets AI training data without violating anyone’s privacy

Apple’s AI efforts don’t have to be hampered by its commitment to user privacy. A blog post published Monday explains how the company can generate the data needed to train its large language models ...
Hosted on MSN

To gain AI visibility, broadcasters must train the LLMs

If an advertiser asked an AI system to build a media plan today, broadcast television would rarely make the recommendation. That’s not because TV is ineffective, but because AI systems cannot see it.
Ars Technica

Researchers show that training on “junk data” can lead to LLM “brain rot”

On the surface, it seems obvious that training an LLM with “high quality” data will lead to better performance than feeding it any old “low quality” junk you can find. Now, a group of researchers is ...