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I wonder if multiple heads having the same activation pattern in a context is related to the limited rank per head; once the VO subspace of each head is saturated with meaningful directions/features maybe the model uses multiple heads to write out features that can't be placed in the subspace of any one head.
Do you have any updates on this? I'm interested in this.
I've trained some sparse MLPs with 20K neurons on a 4L TinyStories model with ReLU activations and no layernorm and I took a look at them after reading this post. For varying integer , I applied an L1 penalty of on the average of the activations per token, which seems pretty close to doing an L1 of on the sum of the activations per token. Your L1 of with 12K neurons is sort of like in my setup. After reading your post, I checked out the cosine similarity between encoder/decoder of original mlp neurons and sparse mlp neurons for varying values of (make sure to scroll down once you click one of the links!):
S=3
https://plotly-figs.s3.amazonaws.com/sparse_mlp_L1_2exp3
S=4
https://plotly-figs.s3.amazonaws.com/sparse_mlp_L1_2exp4
S=5
https://plotly-figs.s3.amazonaws.com/sparse_mlp_L1_2exp5
S=6
https://plotly-figs.s3.amazonaws.com/sparse_mlp_L1_2exp6
I think the behavior you're pointing at is clearly there at lower L1s on layers other than layer 0 (? what's up with that?) and sort of decreases with higher L1 values, to the point that the behavior is there a bit at S=5 and almost not there at S=6. I think the non-dead sparse neurons are almost all interpretable at S=5 and S=6.
Original val loss of model: 1.128 ~= 1.13.
Zero ablation of MLP loss values per layer: [3.72, 1.84, 1.56, 2.07].
S=6 loss recovered per layer
Layer 0: 1-(1.24-1.13)/(3.72-1.13): 96% of loss recovered
Layer 1: 1-(1.18-1.13)/(1.84-1.13): 93% of loss recovered
Layer 2: 1-(1.21-1.13)/(1.56-1.13): 81% of loss recovered
Layer 3: 1-(1.26-1.13)/(2.07-1.13): 86% of loss recovered
Compare to 79% of loss-recovered from Anthropic's A/1 autoencoder with 4K features and a pretty different setup.
(Also, I was going to focus on S=5 MLPs for layers 1 and 2, but now I think I might instead stick with S=6. This is a little tricky because I wouldn't be surprised if tiny-stories MLP neurons are interpretable at higher rates than other models.)
Basically I think sparse MLPs aren't a dead end and that you probably just want a higher L1.
This link is a broken
This is great.
[word] and [word]
can be thought of as "the previous token is ' and'."
It might just be one of a family of linear features or ?? aspect of some other representation ?? corresponding to what the previous token is, to be used for at least induction head.
Maybe the reason you found ' and' first is because ' and' is an especially frequent word. If you train on the normal document distribution, you'll find the most frequent features first.
Consistency models are trained from scratch in the paper in addition to distilled from diffusion models. I think it'll probably just work with text-conditioned generation, but unclear to me w/o much thought how to do the equivalent of classifier-free guidance.
seems related to: https://arxiv.org/pdf/2303.01469.pdf
I think this post is great and I'm really happy that it's published.
I really appreciate this work!
I wonder if the reason MLPs are more polysemantic isn't because there are fewer MLPs than heads but because the MLP matrices are larger--
Suppose the model is storing information as sparse [rays or directions]. Then SVD on large matrices like the token embeddings can misunderstand the model in different ways:
- Many of the sparse rays/directions won't be picked up by SVD. If there are 10,000 rays/directions used by the model and the model dimension is 768, SVD can only pick 768 directions.
- If the model natively stores information as rays, then SVD is looking for the wrong thing: directions instead of rays. If you think of SVD as a greedy search for the most important directions, the error might increase as the importance of the direction decreases.
- Because the model is storing things sparsely, it can squeeze in far more meaningful directions than the model dimension. But these directions can't be perfectly orthogonal, they have to interfere with each other at least a bit. This noise could make SVD with large matrices worse and also means that the assumptions involved in SVD are wrong.
As evidence for the above story, I notice that the earliest PCA directions on the token embeddings are interpretable, but they quickly become less interpretable?
Maybe because the QK/OV matrices have low rank they specialize in a small number of the sparse directions (possibly greater than their rank) and have less interference noise. These could contribute to interpretability of SVD directions.
You might expect in this world that the QK/OV SVD directions would be more interpretable than the MLP matrices which would in turn be more interpretable than the token embedding SVD.
I think at least some GPT2 models have a really high-magnitude direction in their residual stream that might be used to preserve some scale information after LayerNorm. [I think Adam Scherlis originally mentioned or showed the direction to me, but maybe someone else?]. It's maybe akin to the water-droplet artifacts in StyleGAN touched on here: https://arxiv.org/pdf/1912.04958.pdf
We begin by observing that most images generated by StyleGAN exhibit characteristic blob-shaped artifacts that resemble water droplets. As shown in Figure 1, even when the droplet may not be obvious in the final image, it is present in the intermediate feature maps of the generator.1 The anomaly starts to appear around 64×64 resolution, is present in all feature maps, and becomes progressively stronger at higher resolutions. The existence of such a consistent artifact is puzzling, as the discriminator should be able to detect it. We pinpoint the problem to the AdaIN operation that normalizes the mean and variance of each feature map separately, thereby potentially destroying any information found in the magnitudes of the features relative to each other. We hypothesize that the droplet artifact is a result of the generator intentionally sneaking signal strength information past instance normalization: by creating a strong, localized spike that dominates the statistics, the generator can effectively scale the signal as it likes elsewhere. Our hypothesis is supported by the finding that when the normalization step is removed from the generator, as detailed below, the droplet artifacts disappear completely.
No, I'm pretty confident every expert is a neural network policy trained on the task. See "F. Data Collection Details" and the second paragraph of "3.3. Robotics - RGB Stacking Benchmark (real and sim)"
What software did you use to produce this diagram?
How much influence and ability you expect to have as an individual in that timeline.
For example, I don't expect to have much influence/ability in extremely short timelines, so I should focus on timelines longer than 4 years, with more weight to longer timelines and some tapering off starting around when I expect to die.
How relevant thoughts and planning now will be.
If timelines are late in my life or after my death, thoughts, research, and planning now will be much less relevant to the trajectory of AI going well, so at this moment in time I should weight timelines in the 4-25 year range more.
Value-symmetry: "Will AI systems in the critical period be equally useful for different values?"
This could fail if, for example, we can build AI systems that are very good at optimizing for easy-to-measure values but significantly worse at optimizing for hard to measure values. It might be easy to build a sovereign AI to maximize the profit of a company, but hard to create one that cares about humans and what they want.
Evan Hubinger has some operationalizations of things like this here and here.
Open / Closed: "Will transformative AI systems in the critical period be publicly available?"
A world where everyone has access to transformative AI systems, for example by being able to rent them (like GPT-3's API once it's publicly available), might be very different from one where they are kept private by one or more private organizations.
For example, if strategy stealing doesn't hold, this could dramatically change the distribution of power, because the systems might be more helpful for some tasks and values than others.
This variable could also affect timelines estimates if publicly accessible TAI systems increase GDP growth, among other effects it could have on the world.
Deceptive alignment: “In the critical period, will AIs be deceptive?”
Within the framework of Risks from Learned Optimization, this is when a mesa-optimizer has a different objective than the base objective, but instrumentally optimizes the base objective to deceive humans. It can refer more generally to any scenario where an AI system behaves instrumentally one way to deceive humans.
Alignment tax: “How much more difficult will it be to create an aligned AI vs an unaligned AI when it becomes possible to create powerful AI?”
If the alignment tax is low, people have less incentive to build an unaligned AI as they'd prefer to build a system that's trying to do what they want. Then, to increase the probability that our AI trajectory goes well, one could focus on how to reduce the alignment tax.