StefanHex's Shortform

Are the features learned by the model the same as the features learned by SAEs?

TL;DR: I want true features model-features to be a property of the model weights, and to be recognizable without access to the full dataset. Toy models have that property. My “poor man’s model-features” [LW · GW] have it. I want to know whether SAE-features have this property too, or if SAE-features do not match the true features model-features.

Introduction: Neural networks likely encode features in superposition. That is, features are represented as directions in activation space, and the model likely tracks many more features than dimensions in activation space. Because features are sparse, it should still be possible for the model to recover and use individual feature values.^[1]

Problem statement: The prevailing method for finding these features are Sparse Autoencoders (SAEs). SAEs are well-motivated because they do recover superposed features in toy models. However, I am not certain whether SAEs recover the features of LLMs. I am worried (though not confident) that SAEs do not recover the features of the model (but the dataset), and that we are thus overconfident in how much SAEs tell us.

SAE failure mode: SAEs are trained to achieve a certain compression^[2] task: Compress activations into a sparse overcomplete basis, and reconstruct the original activations based on this compressed representation. The solution to this problem can be identical to what the neural network does (wanting to store & use information), but it not necessarily is. In TMS, the network’s only objective is to compress features, so it is natural that the SAE-features match the model-features. But LLMs solve a different task (well, we don’t have a good idea what LLMs do), and training an SAE on a model’s activations might yield a basis different from the model-features (see hypothetical Example 1 below).

Operationalisation of model-features (I’m tabooing “true features”): In the Toy Model of Superposition (TMS) the model’s weights are clearly adjusted to the features directions. We can tell a feature from looking at the model weights. I want this to be a property of true SAE-features as well. Then I would be confident that the features are a property of the model, and not (only) of the dataset distribution. Concrete operationalisation:

• I give you 5 real SAE-features, and 5 made-up features (with similar properties). Can you tell which features are the real ones? Without relying on the dataset (but you may use an individual prompt). Lindsey (2024) is some evidence, but would it distinguish the SAE-features from an arbitrary decomposition of the activations into 5 fake-features?

Why do I care? I expect that the model-features are, in some sense, the computational units of the model. I expect our understanding to be more accurate (and to generalize) if we understand what the model actually does internally (see hypothetical Example 2 below).

Is this possible? Toy models of computation in superposition seem to suggest that models give special treatment to feature directions (compared to arbitrary activation directions), for example the error correction described here. This may privilege the basis of model-features over other decompositions of activations. I discuss experiment proposals at the bottom.

Example 1: Imagine an LLM was trained on The Pile excluding Wikipedia. Now we train an SAE on the model’s activations on a different dataset including Wikipedia. I expect that the SAE will find Wikipedia-related features: For example, a Wikipedia-citation-syntax feature on a low level, or an Wikipedia-style-objectivity feature on a high level. I would claim that this is not a feature of the model: During training the model never encountered these concepts, it has not reserved a direction in its superposition arrangement (think geometric shapes in Toy Model of Superposition) for this feature.

• It feels like there is a fundamental distinction between a model (SGD) “deciding” whether to learn a feature (as it does in TMS) and an SAE finding a feature that was useful for compressing activations.

Example 2: Maybe an SAE trained on an LLM playing Civilization and Risk finds a feature that corresponds to “strategic deception” on this dataset. But actually the model does not use a “strategic deception” feature (instead strategic deception originates from some, say, the “power dynamics” feature), and it just happens that the instances of strategic deception in those games clustered into a specific direction. If we now take this direction to monitor for strategic deception we will fail to notice other strategic deception originating from the same “power dynamics” features.

• If we had known that the model-features that were active during the strategic deception instances were the “power dynamics” (+ other) features, we would have been able to choose the right, better generalizing, deception detection feature.

Experiment proposals: I have explored [LW · GW] the abnormal effect that “poor man’s model-features” (sampled as the difference between two independent model activations) have on model outputs, and their relation to theoretically predicted noise suppression in feature activations. Experiments in Gurnee (2024) [LW · GW] and Lindsey (2024) suggest that SAE decoder errors and SAE-features also have an abnormal effect on the model.  With the LASR Labs [LW · GW] team I mentor I want to explore whether SAE-features match the theoretical predictions, and whether the SAE-feature effects match those expected from model-features.

1. ^{^}

   I’ll ignore the “there’s more to activations than just features” point made e.g. here [LW · GW], that’s a separate discussion.

2. ^{^}

   I know the SAE basis is larger, but it is enforced to be sparse and thus cannot perfectly store the activations.

Has anyone tested whether feature splitting can be explained by composite (non-atomic) features [LW · GW]?

• Feature splitting is the observation that SAEs with larger dictionary size find features that are geometrically (cosine similarity) and semantically (activating dataset examples) similar. In particular, a larger SAE might find multiple features that are all similar to each other, and to a single feature found in a smaller SAE.
  • Anthropic gives the example of the feature " 'the' in mathematical prose" which splits into features " 'the' in mathematics, especially topology and abstract algebra" and " 'the' in mathematics, especially complex analysis" (and others).

There’s at least two hypotheses for what is going on.

1. The “true features” are the maximally split features; the model packs multiple true features into superposition close to each other. Smaller SAEs approximate multiple true features as one due to limited dictionary size.
2. The “true features” are atomic features, and split features are composite features made up of multiple atomic features. Feature splitting is an artefact of training the model for sparsity, and composite features could be replaced by linear combinations of a small number of other (atomic) features.

Anthropic conjectures hypothesis 1 in Towards Monosemanticity. Demian Till argues for hypothesis 2 in this post [LW · GW]. I find Demian’s arguments compelling. They key idea is that an SAE can achieve lower loss by creating composite features for frequently co-occurring concepts: The composite feature fires instead of two (or more) atomic features, providing a higher sparsity (lower sparsity penalty) at the cost of taking up another dictionary entry (worse reconstruction).

• I think the composite feature hypothesis is plausible, especially in light of Anthropic’s Feature Completeness results in Scaling Monosemanticity. They find that not all model concepts are represented in SAEs, and that rarer concepts are less likely to be represented (they find an intriguing relation between number of alive features and feature frequency required to be represented in the SAE, likely related to the frequency-rank via Zipf’s law). I find it probably that the optimiser may dedicate extra dictionary entries to composite features of high-frequency concepts at the cost of representing low-frequency concepts.
• This is bad for interpretability not (only) because low-frequency concepts are omitted, but because the creation of composite features requires the original atomic features to not fire anymore in the composite case.
  • Imagine there is a “deception” feature, and a “exam” feature. How deception in exams is quite common, so the model learns a composite “deception in the context of exams” feature, and the atomic “deception” and “exam” features no longer fire in that case.
  • Then we can no longer use the atomic “deception” SAE direction as a reliable detector of deception, because it doesn’t fire in cases where the composite feature is active!

Do we have good evidence for the one or the other case?

We observe that split features often have high cosine similarity, but this is explained by both hypotheses. (Anthropic says features are clustered together because they’re similar. Demian Till’s hypothesis would claim that multiple composite features contain the same atomic features, again explaining the similarity.)

A naive test may be to test whether features can be explained by a sparse linear combination of other features, though I’m not sure how easy this would be to test.

For reference, cosine similarity of SAE decoder directions in Joseph Bloom's GPT2-small SAEs, blocks.1.hook_resid_pre and blocks.10.hook_resid_pre  compared to random directions and random directions with the same covariance as typical activations.

I think we should think more about computation in superposition. What does the model do with features? How do we go from “there are features” to “the model outputs sensible things”? How do MLPs retrieve knowledge (as commonly believed) in a way compatible with superposition (knowing more facts than number of neurons)?

This post [LW · GW] (and paper) by @Kaarel [LW · GW], @jake_mendel [LW · GW], @Dmitry Vaintrob [LW · GW] (and @LawrenceC [LW · GW]) is the kind of thing I'm looking for, trying to lay out a model of how computation in superposition could work. It makes somewhat-concrete predictions [LW(p) · GW(p)] about the number and property of model features.

Why? Because (a) these feature properties may help us find the features of a model (b) a model of computation may be necessary if features alone are not insufficient to address AI Safety (on the interpretability side).