Description du poste
Recently Goodfire has come out swinging with two posts indicating that neurons in transformers are "more interpretable than previously assumed", or simply "already clean". In the "Neurons as Interpretable Basis" post they offer three reasons for this claim:
• First, neurons are already functionally sparse - pruning 92 % of the weakest MoE neuron activations only increases the loss by 2.9%• Second, the role of a neuron can be split into different "computational functions". Effectively, we can name the contexts in which a neuron fires, and assign reasons to those firings. Most neurons only have one context-reason grouping.• Finally, we can produce automated natural language explanations of neuron firings that are predictive of their behaviour.First, I would like to congratulate them for their detailed work and breakdown, complete with well-designed graphics. I found the work interesting and very well thought through. It is a shame that we have no idea if these techniques apply to lab models with non-public architectures, but I think it is fairly reasonable to say that at least some of this work is transferable directly (perhaps until a fully recurrent architecture is the norm instead of looped transformers).
Next, I would like to consider the original motivations for interpretability. In A Mathematical Framework for Transformer Circuits, Elhage et al. write:
Transformer language models are an emerging technology that is gaining increasingly broad real-world use, for example in systems like GPT-3, LaMDA, Codex, Meena, Gopher, and similar models. However, as these models scale, their open-endedness and high capacity creates an increasing scope for unexpected and sometimes harmful behaviors. Even years after a large model is trained, both creators and users routinely discover model capabilities – including problematic behaviors – they were previously unaware of.
One avenue for addressing these issues is mechanistic interpretability, attempting to reverse engineer the d