TLDR: We don't have to hope for generalization of our oversight procedures. Instead, we can 1) define a proxy failure that we can evaluate and 2) worst-case against our oversight procedure on the actual distribution we care about (but using the proxy failure so that we have ground truth).
Causal Scrubbing: My main problem with causal scrubbing as a solution here is that only guarantees the sufficiency, but not the necessity, or your explanation. As a result, my understanding is that a causal-scrubbing-based evaluation would admit a trivial explanation that simply asserts that the entire model is relevant for every behavior.
Redwood has been experimenting with learning (via gradient descent) causal scrubbing explanations that are somewhat addressing your necessity point. Specifically:
This approach doesn't address all the concerns one might have with using causal scrubbing to understand models, but just wanted to flag that this is something we're thinking about as well.
Yes! The important part is decomposing activations (not neccessarily linearly). I can rewrite my MLP as:
MLP(x) = f(x) + (MLP(x) - f(x))
and then claim that the MLP(x) - f(x) term is unimportant. There is an example of this in the parentheses balancer example.
Nice summary! One small nitpick:> In the features framing, we don’t necessarily assume that features are aligned with circuit components (eg, they could be arbitrary directions in neuron space), while in the subgraph framing we focus on components and don’t need to show that the components correspond to features
This feels slightly misleading. In practice, we often do claim that sub-components correspond to features. We can "rewrite" our model into an equivalent form that better reflects the computation it's performing. For example, if we claim that a certain direction in an MLP's output is important, we could rewrite the single MLP node as the sum of the MLP output in the direction + the residual term. Then, we could make claims about the direction we pointed out and also claim that the residual term is unimportant.
The important point is that we are allowed to rewrite our model however we want as long as the rewrite is equivalent.
Good question! As you suggest in your comment, increasing marginal returns to capacity induce monosemanticity, and decreasing marginal returns induce polysemanticity.
We observe this in our toy model. We didn't clearly spell this out in the post, but the marginal benefit curves labelled from A to F correspond to points in the phase diagram. At the top of the phase diagram where features are dense, there is no polysemanticity because the marginal benefit curves are increasing (see curves A and B). In the feature sparse region (points D, E, F), we see polysemanticity because the marginal benefit curves are decreasing.
The relationship between increasing/decreasing marginal returns and polysemanticity generalizes beyond our toy model. However, we don't have a generic technique to define capacity across different architectures and loss functions. Without a general definition, it's not immediately obvious how to regularize the loss for increasing returns to capacity.
You're getting at a key question the research brings up: can we modify the loss function to make models more monosemantic? Empirically, increasing sparsity increases polysemanticity across all models we looked at (figure 7 from the arXiv paper)*. According to the capacity story, we only see polysemanticity when there is decreasing marginal returns to capacity. Therefore, we hypothesize that there is likely a fundamental connection between feature sparsity and decreasing marginal returns. That is to say, we are suggesting that: if features are sparse and similar enough in importance, polysemanticity is optimal.
*Different models showed qualitatively different levels of polysemanticity as a function of sparsity. It seems possible that tweaking the architecture of a LLM could change the amount of polysemanticity, but we might take a performance hit for doing so.