Jacob Steinhardt

Assistant Professor

Stanford University,UC Berkeley

Jacob Steinhardt is an Assistant Professor in Statistics at UC Berkeley. His work focuses on robustness, reward specification and scalable alignment of machine learning (ML) systems.

Publications

Eliciting Latent Predictions from Transformers with the Tuned Lens

Interpretability

The tuned lens learns an affine transformation to decode the activations of each layer of a transformer as next-token predictions. This provides insights into how model predictions are refined layer by layer. We validate our method on various autoregressive language models up to 20B parameters, showing it to be more predictive, reliable and unbiased than the logit lens baseline.

March 14, 2023
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Research

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