1 citations · 1 across the 1 of their papers we have counts for
4 papers
Just-in-time and distributed task representations in language models
Yuxuan Li, Declan Campbell, Stephanie C. Y. Chan +1
Many of language models' impressive capabilities originate from their in-context learning: based on instructions or examples, they can infer and perform new tasks without weight up…
Representation biases: will we achieve complete understanding by analyzing representations?
Andrew Kyle Lampinen, Stephanie C. Y. Chan, Yuxuan Li +1
A common approach in neuroscience is to study neural representations as a means to understand a system -- increasingly, by relating the neural representations to the internal repre…
On the generalization of language models from in-context learning and finetuning: a controlled study
Andrew K. Lampinen, Arslan Chaudhry, Stephanie C. Y. Chan +7
Large language models exhibit exciting capabilities, yet can show surprisingly narrow generalization from finetuning. E.g. they can fail to generalize to simple reversals of relati…
The broader spectrum of in-context learning
Andrew Kyle Lampinen, Stephanie C. Y. Chan, Aaditya K. Singh +1
The ability of language models to learn a task from a few examples in context has generated substantial interest. Here, we provide a perspective that situates this type of supervis…