This is a comment from Andy Zou, who led the RepE paper but doesn’t have a LW account:
“Yea I think it's fair to say probes is a technique under rep reading which is under RepE (https://www.ai-transparency.org/). Though I did want to mention, in many settings, LAT is performing unsupervised learning with PCA and does not use any labels. And we find regular linear probing often does not generalize well and is ineffective for (causal) model control (e.g., details in section 5). So equating LAT to regular probing might be an oversimplification. How to best eli... (read more)
What's the relationship between this method and representation engineering? They seem quite similar, though maybe I'm missing something. You train a linear probe on a model's activations at a particular layer in order to distinguish between normal forward passes and catastrophic ones where the model provides advice for theft.
Representation engineering asks models to generate both positive and negative examples of a particular kind of behavior. For example, the model would generate outputs with and without theft, or with and without general power-seek... (read more)
Probes fall within the representation engineering monitoring framework.
LAT (the specific technique they use to train probes in the RePE paper) is just regular probe training, but with a specific kind of training dataset ((positive, negative) pairs) and a slightly more fancy loss. It might work better in practice, but just because of better inductive biases, not because something fundamentally different is going on (so arguments against coup probes mostly apply if LAT is used to train them). It also makes creating a dataset slightly more annoying - especial... (read more)
Great resource, thanks for sharing! As somebody who's not too deeply familiar with either mechanistic interpretability or the academic field of interpretability, I find myself confused by the fact that AI safety folks usually dismiss the large academic field of interpretability. Most academic work on ML isn't useful for safety because safety studies different problems with different kinds of systems. But unlike focusing on worst-case robustness or inner misalignment, I would expect generating human understandable explanations of what neural networks are do... (read more)
These professors all have a lot of published papers in academic conferences. It’s probably a bit frustrating to not have their work summarized, and then be asked to explain their own work, when all of their work is published already. I would start by looking at their Google Scholar pages, followed by personal websites and maybe Twitter. One caveat would be that papers probably don’t have full explanations of the x-risk motivation or applications of the work, but that’s reading between the lines that AI safety people should be able to do themselves.
Agree with both aogara and Eli's comment.
One caveat would be that papers probably don’t have full explanations of the x-risk motivation or applications of the work, but that’s reading between the lines that AI safety people should be able to do themselves.
For me this reading between the lines is hard: I spent ~2 hours reading academic papers/websites yesterday and while I could quite quickly summarize the work itself, it was quite hard to me to figure out the motivations.
Ah okay. Are there theoretical reasons to think that neurons with lower variance in activation would be better candidates for pruning? I guess it would be that the effect on those nodes is similar across different datapoints, so they can be pruned and their effects will be replicated by the rest of the network.
“…nodes with the smallest standard deviation.” Does this mean nodes whose weights have the lowest absolute values?