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Teaching large language models how to absorb new knowledge

MIT researchers developed a technique that enables LLMs to permanently absorb new knowledge by generating study sheets based on data the model uses to memorize important information.

Google’s ‘Nested Learning’ paradigm could solve AI's memory and continual learning problem

Inspired by how the human brain consolidates memory, the 'Nested Learning' framework allows different parts of a model to ...

New software toolbox enables brain-like models to learn directly from data

Researchers have developed a powerful new software toolbox that allows realistic brain models to be trained directly on data.
Wired

Large Language Models’ Emergent Abilities Are a Mirage

The original version of this story appeared in Quanta Magazine. Two years ago, in a project called the Beyond the Imitation Game benchmark, or BIG-bench, 450 researchers compiled a list of 204 tasks ...

A unified model of memory and perception: How Hebbian learning explains our recall of past events

A collaboration between SISSA's Physics and Neuroscience groups has taken a step forward in understanding how memories are ...
Microsoft

Tiny but mighty: The Phi-3 small language models with big potential

Sometimes the best way to solve a complex problem is to take a page from a children’s book. That’s the lesson Microsoft researchers learned by figuring out how to pack more punch into a much smaller ...