Large language models

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

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.

Researchers uncover striking parallels in the ways that humans and machine learning models acquire language skills.

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.

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

It’s simple enough for AI to seem to comprehend data, but devising a true test of a machine’s knowledge has proved difficult.

Melanie Mitchell has worked on digital minds for decades. She says they’ll never truly be like ours until they can make analogies.