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in
Workshop: CogInterp: Interpreting Cognition in Deep Learning Models

Mechanisms of Symbol Processing in Transformers

Paul Smolensky ⋅ Roland Fernandez ⋅ Zhenghao Herbert Zhou ⋅ Mattia Opper ⋅ Adam Davies ⋅ Jianfeng Gao

Project Page [ OpenReview]

Abstract

We construct a 100% mechanistically-explainable transformer which perfectly performs an in-context learning task that requires inferring, and then reasoning over, latent syntactic structure. It implements a program in a symbolic, Turing-complete language in a family of leading models of the human cognitive architecture.

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