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Causal evaluation reveals that relying on correlational analysis can lead to significant misinterpretations of task learnability in language models.
Surprisal theory's reliance on arbitrary tokenization schemes undermines its validity, but this framework offers a way to fix it.
Repurpose existing language models for entirely new output formats like bytes, words, or even DNA sequences, all without retraining, by wrapping them in a finite-state transducer.
