Mechanistic Interpretability for Transformers in Financial Engineering
Edward Sharkey, Philip Colin Treleaven
2025 · 1 citations
Mechanistic interpretability[9] is the study of taking a trained neural network, and analysing the weights to reverse engineer the algorithms learned by the mode. We propose multiple forms of mechanistic interpretability MI [9] to support financial engineering utilising large language models. As these models scale, their open-endedness and high capacity creates an increasing scope for unexpected and sometimes harmful behaviors. [9] [1] Antrhopic discuss even years after a large model is trained, both creators and users routinely discover model capabilities including problematic behaviors they…
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