Natural language processing

Neural networks and other forms of machine learning ultimately learn by trial and error, one improvement at a time.

Researchers are drawing on ideas from game theory to improve large language models and make them more consistent.

The computer scientist Ellie Pavlick is translating philosophical concepts such as “meaning” into concrete, testable ideas.

Large language models do better at solving problems when they show their work. Researchers are beginning to understand why.

Erasing key information during training results in machine learning models that can learn new languages faster and more easily.

A new study suggests that so-called emergent abilities actually develop gradually and predictably, depending on how you measure them.

Far from being “stochastic parrots,” the biggest large language models seem to learn enough skills to understand the words they’re processing.

To better understand how neural networks learn to simulate writing, researchers trained simpler versions on synthetic children’s stories.

Number theorist Andrew Granville on what mathematics really is — and why objectivity is never quite within reach.