Mechanistic interpretability

Machine learning and data mining
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Mechanistic interpretability (often shortened to mech interp or MI) is a subfield of research within explainable artificial intelligence which seeks to fully reverse-engineer neural networks (akin to reverse-engineering a compiled binary of a computer program), with the ultimate goal of understanding the mechanisms underlying their computations.^[1]^[2]^[3] The field is particularly focused on large language models.

^ Olah, Chris (June 2022). "Mechanistic Interpretability, Variables, and the Importance of Interpretable Bases". Transformer Circuits Thread. Anthropic. Retrieved 28 March 2025.
^ Olah, Chris; Cammarata, Nick; Schubert, Ludwig; Goh, Gabriel; Petrov, Michael; Carter, Shan (2020). "Zoom In: An Introduction to Circuits". Distill. 5 (3). doi:10.23915/distill.00024.001.
^ Cite error: The named reference mathematical was invoked but never defined (see the help page).

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