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cs.LG2026
Interpreting Language Model Parameters
Lucius Bushnaq, Dan Braun, Oliver Clive-Griffin +5
Mechanistic interpretability aims to uncover how neural networks use their parameters to implement their impressive neural algorithms. Although previous work has uncovered substant…
cs.LG2026
Interactions Between Crosscoder Features: A Compact Proofs Perspective
Dmitry Manning-Coe, Thomas Read, Anna Soligo +4
Dictionary learning methods like Sparse Autoencoders (SAEs) and crosscoders attempt to explain a model by decomposing its activations into independent features. Interactions betwee…