EurekAlert!

A perspective on developing foundation models for analyzing spatial transcriptomic data

Foundation models (FMs), which are deep learning models pretrained on large-scale data and applied to diverse downstream ...
Science Daily

Like human brains, large language models reason about diverse data in a general way

Researchers find large language models process diverse types of data, like different languages, audio inputs, images, etc., similarly to how humans reason about complex problems. Like humans, LLMs ...
Forbes

Here’s How Big LLMs Teach Smaller AI Models Via Leveraging Knowledge Distillation

Forbes contributors publish independent expert analyses and insights. Dr. Lance B. Eliot is a world-renowned AI scientist and consultant. In today’s column, I examine the rising tendency of employing ...
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 ...
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 ...