2024/06/17 by Jason N. Gross, Gross, Jason, Rajashree Agrawal +13 · 1 citation
Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #Logic in Computer Science (cs.LO) #Machine Learning (cs.LG) #Machine Learning and Algorithms #Model Reduction and Neural Networks
paper · pdf · doi:10.48550/arxiv.2406.11779
openalex publication_date 2024/06/17 · openalex created_date 2024/06/20 · openalex updated_date 2026/07/28
We propose using mechanistic interpretability -- techniques for reverse engineering model weights into human-interpretable algorithms -- to derive and compactly prove formal guarantees on model performance. We prototype this approach by formally proving accuracy lower bounds for a small transformer trained on Max-of-K, validating proof transferability across 151 random seeds and four values of K. We create 102 different computer-assisted proof strategies and assess their length and tightness of bound on each of our models. Using quantitative metrics, we find that shorter proofs seem to require and provide more mechanistic understanding. Moreover, we find that more faithful mechanistic understanding leads to tighter performance bounds. We confirm these connections by qualitatively examining a subset of our proofs. Finally, we identify compounding structureless errors as a key challenge for using mechanistic interpretability to generate compact proofs on model performance.