Learning path and canonical resources for Mechanistic Interpretability

pasta_paul

Recently I read Julian Mendel's article, " Mechanistic Interpretability: Peeking Inside an LLM " on Towards Data Science( https://towardsdatascience.com/mechanistic-interpretability-peeking-inside-an-llm/ ), and I became fascinated by the idea of moving beyond treating Large Language Models as "black boxes." Summary of the article's premise: The post proposes that we can understand and even manipulate an LLM’s behavior by examining its internal...

Originally asked by Humberto José Bortolossi on Stack Exchange (ai) (CC BY-SA 4.0).

uphilltech

Great question — answering as the UphillTech team:

Thanks for the question. On top of my head, I can think of this ARENA tutorial on transformer interpretability: https://learn.arena.education/chapter1_transformer_interp/ Someone established and well-acquainted with the field might give more significant resources. Best of luck for your reseach journey!

Full answer by ZKMathquant: https://ai.stackexchange.com/a/50617 (CC BY-SA 4.0).

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