Seven transformer theories fall short of explaining what models learn

Controlled task comparisons expose mismatches between theoretical capability predictions, training success and generalization to longer inputs.

Big Tech
Awni Altabaa · Tobias Schnabel · Kiran Tomlinson

Yale University · Microsoft Research

Research Digest··2 min read
Illustration: Seven transformer theories fall short of explaining what models learn
Altabaa, Schnabel and Tomlinson compare seven theories of transformer capability against experiments with small, task-specific transformers and pretrained language models.

The authors translated seven frameworks, Star-free, AC⁰, TC⁰, C-RASP, MPC, BAPO and Sensitivity, into a common matrix of task predictions.

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