Natural language processing

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.

In steganography, an ordinary message masks the presence of a secret communication. Humans can never do it perfectly, but a new study shows it’s possible for machines.

Today’s language models are more sophisticated than ever, but they still struggle with the concept of negation. That’s unlikely to change anytime soon.

By imbuing enormous vectors with semantic meaning, we can get machines to reason more abstractly — and efficiently — than before.

Large language models like ChatGPT are now big enough that they’ve started to display startling, unpredictable behaviors.

Large language models still struggle with basic reasoning tasks. Two new papers that apply machine learning to math provide a blueprint for how that could change.

A wave of research improves reinforcement learning algorithms by pre-training them as if they were human.