Thanks for writing this - I've found it useful in my current attempts to survey some key mechanistic interpretability literature.
a decent survey paper on what’s up in the rest of interpretability.I’m personally pretty meh about the majority of the academic field of interpretability
a decent survey paper on what’s up in the rest of interpretability.
I’m personally pretty meh about the majority of the academic field of interpretability
A bit confused by this. This paper's abstract and intro claim to be focusing on inner interpretability methods - which they define as learned features and internal structure. This seems to fit my idea of what mechanistic interpretability is pretty well, but you seem to classify it as 'the rest of interpretability'.
Do you see a clear distinction between mechanistic interpretability methods vs the methods reviewed in this paper? If so, what's the distinction?
Nice paper, thanks! A meta question - how did you analyse and systematise the results of over 300 papers? (gesturing at software tools/general methodology here)