Forbes

What Happens When LLM’s Run Out Of Useful Data?

Most of us feel like we’re drowning in data. And yet, in the world of generative AI, a looming data shortage is keeping some researchers up at night. GenAI is unquestionably a technology whose ...
VentureBeat

Fine-tuning vs. in-context learning: New research guides better LLM customization for real-world tasks

Two popular approaches for customizing large language models (LLMs) for downstream tasks are fine-tuning and in-context learning (ICL). In a recent study, researchers at Google DeepMind and Stanford ...
Lupus Science & Medicine

Harnessing AI and social media to understand real-world patient experiences in SLE

Objective To apply large language models (LLMs) to Reddit posts referencing SLE to identify patient-expressed unmet medical ...
Search Engine Land

LLM optimization in 2026: Tracking, visibility, and what’s next for AI discovery

Marketing, technology, and business leaders today are asking an important question: how do you optimize for large language models (LLMs) like ChatGPT, Gemini, and Claude? LLM optimization is taking ...
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 ...
techtimes

LLM Data Mixture Breaks When Training Pools Shift: Causal Inference Offers Fix

Every large language model begins as a question of proportion. How much web text? How much code? How much scientific literature? The ratio of training data across these domains — what researchers call ...
9to5Mac

New Apple study shows LLMs can tell what you’re doing from audio and motion data

Apple researchers have published a study that looks into how LLMs can analyze audio and motion data to get a better overview of the user’s activities. Here are the details. They’re good at it, but not ...