Ryan Cotterell

Causal evaluation reveals that relying on correlational analysis can lead to significant misinterpretations of task learnability in language models.

Recurrent neural networks can exhibit drastically different expressivity based on the arithmetic model used, revealing hidden capabilities tied to quantization methods.

Surprisal theory's reliance on arbitrary tokenization schemes undermines its validity, but this framework offers a way to fix it.