Large language models

Large language models such as ChatGPT come with filters to keep certain info from getting out. A new mathematical argument shows that systems like this can never be completely safe.

Is language core to thought, or a separate process? For 15 years, the neuroscientist Ev Fedorenko has gathered evidence of a language network in the human brain — and has found some parallels to LLMs.

Marijn Heule turns mathematical statements into something like Sudoku puzzles, then has computers go to work on them. His proofs have been called “disgusting,” but they go beyond what any human can do.

If language is what makes us human, what does it mean now that large language models have gained “metalinguistic” abilities?

Naomi Saphra thinks that most research into language models focuses too much on the finished product. She’s mining the history of their training for insights into why these systems work the way they do.

You’re carrying around in your head a model of how the world works. Will AI systems need to do the same?

The new science of “emergent misalignment” explores how PG-13 training data — insecure code, superstitious numbers or even extreme-sports advice — can open the door to AI’s dark side.

Fundamental technique lets researchers use a big, expensive “teacher” model to train a “student” model for less.

AI may sound like a human, but that doesn’t mean that AI learns like a human. In this episode, Ellie Pavlick explains why understanding how LLMs can process language could unlock deeper insights into both AI and the human mind.