Intepretability
Views my own
Arthur Conmy
Karma: 998
Oh oops, thanks so much. We’ll update the paper accordingly. Nit: it’s actually
(it’s just minimizing a quadratic)
ETA: the reason we have complicated equations is that we didn’t compute during training (this quantity is kinda weird). However, you can compute from quantities that are usually tracked in SAE training. Specifically, and all terms here are clearly helpful to track in SAE training.
Improving Dictionary Learning with Gated Sparse Autoencoders
We haven’t tried this yet. Thanks, that’s a good hypothesis.
I suspect that the mean centering paper https://arxiv.org/abs/2312.03813 is just cancelling the high frequency features, and if so this is a good explanation for why taking differences is important in activation steering.
(Though it doesn’t explain why the SAEs learn several high frequency features when trained on the residual stream)
Yes, pretty much.
There’s some work on transferring steering vecs, e.g. the Llama-2 steering paper (https://arxiv.org/abs/2312.06681) shows that you can transfer steering vecs from base to chat model, and I saw results at a Hackathon once that suggested you can train resid stream SAEs on early layers and transfer them to some later layers, too. But retraining is likely what our follow up work will do (this post only used two different SAEs)
[Full Post] Progress Update #1 from the GDM Mech Interp Team
[Summary] Progress Update #1 from the GDM Mech Interp Team
Why is CE loss >= 5.0 everywhere? Looking briefly at GELU-1L over 128 positions (a short sequence length!) I see our models get 4.3 CE loss. 5.0 seems really high?
Ah, I see your section on this, but I doubt that bad data explains all of this. Are you using a very small sequence length, or an odd dataset?
From my perspective this term appeared around 2021 and became basically ubiquitous by 2022
I don’t think this is correct. To add to Steven’s answer, in the “GPT-1” paper from 2018 the abstract discusses...generative pre-training of a language model
on a diverse corpus of unlabeled text, followed by discriminative fine-tuning on each
specific taskand the assumption at the time was that the finetuning step was necessary for the models to be good at a given task. This assumption persisted for a long time with academics finetuning BERT on tasks that GPT-3 would eventually significantly outperformed them on. You can tell this from how cautious the GPT-1 authors are about claiming the base model could do anything, and they sound very quaint:
> We’d like to better understand why language model pre-training of transformers is effective. A hypothesis is that the underlying generative model learns to perform many of the tasks we evaluate on in order to improve its language modeling capability
We Inspected Every Head In GPT-2 Small using SAEs So You Don’t Have To
The fact that Pythia generalizes to longer sequences but GPT-2 doesn’t isn’t very surprising to me—getting long context generalization to work is a key motivation for rotary, e.g. the original paper https://arxiv.org/abs/2104.09864
Do you apply LR warmup immediately after doing resampling (i.e. immediately reducing the LR, and then slowly increasing it back to the normal value)? In my GELU-1L blog post I found this pretty helpful (in addition to doing LR warmup at the start of training)
Attention SAEs Scale to GPT-2 Small
(This reply is less important than my other)
> The network itself doesn’t have a million different algorithms to perform a million different narrow subtasks
For what it’s worth, this sort of thinking is really not obvious to me at all. It seems very plausible that frontier models only have their amazing capabilities through the aggregation of a huge number of dumb heuristics (as an aside, I think if true this is net positive for alignment). This is consistent with findings that e.g. grokking and phase changes are much less common in LLMs than toy models.
(Two objections to these claims are that plausibly current frontier models are importantly limited, and also that it’s really hard to prove either me or you correct on this point since it’s all hand-wavy)
Thanks for the first sentence—I appreciate clearly stating a position.
measured over a single token the network layers will have representation rank 1
I don’t follow this. Are you saying that the residual stream at position 0 in a transformer is a function of the first token only, or something like this?
If so, I agree—but I don’t see how this applies to much SAE[1] or mech interp[2] work. Where do we disagree?
- ^
E.g. in this post here we show in detail how an “inside a question beginning with which” SAE feature is computed from which and predicts question marks (I helped with this project but didn’t personally find this feature)
- ^
More generally, in narrow distribution mech interp work such as the IOI paper, I don’t think it makes sense to reduce the explanation to single-token perfect accuracy probes since our explanation generalises fairly well (e.g. the “Adversarial examples” in Section 4.4 Alexandre found, for example)
- ^
Neel and I recently tried to interpret a language model circuit by attaching SAEs to the model. We found that using an L0=50 SAE while only keeping the top 10 features by activation value per prompt (and zero ablating the others) was better than an L0=10 SAE by our task-specific metric, and subjective interpretability. I can check how far this generalizes.
If you have <98% perf explained (on webtext relative to unigram or bigram baseline), then you degrade from GPT4 perf to GPT3.5 perf
Two quick thoughts on why this isn’t as concerning to me as this dialogue emphasized.
1. If we evaluate SAEs by the quality of their explanations on specific narrow tasks, full distribution performance doesn’t matter2. Plausibly the safety relevant capabilities of GPT (N+1) are a phase change from GPT N, meaning much larger loss increases in GPT (N+1) when attaching SAEs are actually competitive with GPT N (ht Tom for this one)
Is the drop of eval loss when attaching SAEs a crux for the SAE research direction to you? I agree it’s not ideal, but to me the comparison of eval loss to smaller models only makes sense if the goal of the SAE direction is making a full-distribution competitive model. Explaining narrow tasks, or just using SAEs for monitoring/steering/lie detection/etc. doesn’t require competitive eval loss. (Note that I have varying excitement about all these goals, e.g. some pessimism about steering)
> 0.3 CE Loss increase seems quite substantial? A 0.3 CE loss increase on the pile is roughly the difference between Pythia 410M and Pythia 2.8B
My personal guess is that something like this is probably true. However since we’re comparing OpenWebText and the Pile and different tokenizers, we can’t really compare the two loss numbers, and further there is not GPT-2 extra small model so currently we can’t compare these SAEs to smaller models. But yeah in future we will probably compare GPT-2 Medium and GPT-2 Large with SAEs attached to the smaller models in the same family, and there will probably be similar degradation at least until we have more SAE advances.
We use learning rate 0.0003 for all Gated SAE experiments, and also the GELU-1L baseline experiment. We swept for optimal baseline learning rates on GELU-1L for the baseline SAE to generate this value.
For the Pythia-2.8B and Gemma-7B baseline SAE experiments, we divided the L2 loss by E||x||2, motivated by wanting better hyperparameter transfer, and so changed learning rate to 0.001 or 0.00075 for all the runs (currently in Figure 1, only attention output pre-linear uses 0.00075. In the rerelease we’ll state all the values used). We didn’t see noticable difference in the Pareto frontier changing between 0.001 and 0.0075 so did not sweep the baseline hyperparameter further than this.