I am quite proud of this project and since I consider myself the target audience for HackerNews did I think that maybe some of you would appreciate this open research replication as well. Happy to answer any questions or face any feedback.
Cheers
[1] https://transformer-circuits.pub/2024/scaling-monosemanticit...
Will take a closer look later but if you are hanging around now, it might be worth asking this now. I read this blog post recently:
https://adamkarvonen.github.io/machine_learning/2024/06/11/s...
And the author talks about challenges with evaluating SAEs. I wonder how you tackled that and where to look inside your repo for understanding the your approach around that if possible.
Thanks again!
Assuming you already solved the problem of finding multiple perfect SAE architectures and you trained them to perfection (very much an interesting ML engineering problem that this SAE project attempts to solve) then deciding on which SAE is better comes down to which SAE performs better on the metrics of your automated interpretability methodology. Particularly OpenAI's methodology emphasizes this automated interpretability at scale utilizing a lot of technical metrics upon which the SAEs can be scored _and thereby evaluated_.
Since determining the best metrics and methodology is such an open research question that I could've experimented on for a few additional months, have I instead opted for a simple approach in this first release. I am talking about my and OpenAI's methodology and the differences between both in chapter 4. Interpretability Analysis [1] in my Implementation Details & Results section. I can also recommend reading the OpenAI paper directly or visiting Anthropics transformer-circuits.pub website that often publishes smaller blog posts on exactly this topic.
[1] https://github.com/PaulPauls/llama3_interpretability_sae#4-i... [2] https://transformer-circuits.pub/
Rhetoric isn’t reasoning. True explainability, like what overfitted Sparse Autoencoders claim they offer, basically results in the causal sequence of “thoughts” the model went through as it produces an answer. It’s the same way you may have a bunch of ephemeral thoughts in different directions while you think about anything.
But there is one thing in particular that I’ll acknowledge as a great insight and the beginnings of a very plausible research agenda: bounded near orthogonal vector spaces are wildly counterintuitive in high dimensions and there are existing results around it that create scope for rigor [1].
[1] https://en.m.wikipedia.org/wiki/Johnson%E2%80%93Lindenstraus...
"But our results may also be of broader interest. We find preliminary evidence that superposition may be linked to adversarial examples and grokking, and might also suggest a theory for the performance of mixture of experts models. More broadly, the toy model we investigate has unexpectedly rich structure, exhibiting phase changes, a geometric structure based on uniform polytopes, "energy level"-like jumps during training, and a phenomenon which is qualitatively similar to the fractional quantum Hall effect in physics, among other striking phenomena. We originally investigated the subject to gain understanding of cleanly-interpretable neurons in larger models, but we've found these toy models to be surprisingly interesting in their own right."
I believe that's why humans reason too. We make snap judgements and then use reason to try to convince others of our beliefs. Can't recall the reference right now but they argued that it's really a tool for social influence. That also explains why people who are good at it find it hard to admit when they are wrong - they're not used to having to do it because they can usually out argue others. Prominent examples are easy to find - X marks de spot.
https://youtu.be/wLE71i4JJiM?feature=shared
Sometimes our cortex is in charge, sometimes other parts of our brain are, and we can't tell the difference. Regardless, if we try to justify it later, that justification isn't always coherent because we're not always using the part of our brain we consider to be rational.
Two things that caught my eye were (i) your loss curves and (ii) the assessment of dead latents. Our team also studied SAEs -- trained to reconstruct dense embeddings of paper abstracts rather than individual tokens [1]. We observed a power-law scaling of the lower bound of loss curves, even when we varied the sparsity level and the dimensionality of the SAE latent space. We also were able to totally mitigate dead latents with an auxiliary loss, and we saw smooth sinusoidal patterns throughout training iterations. Not sure if these were due to the specific application we performed (over paper abstracts embeddings) or if they represent more general phenomena.
can I please ask a wacky question that I have about mech.interp. ?
we know that when we use a metric to measure humans, we game the metric, I wonder about future ai, gaming mech.interp.
for simplicity let me propose a 2d matrix to encode tokens, and hope my understanding of neural networks is good enough for this to be a valid question I understand that higher dimensions have a lot more 'space', so accidental collisions are easily avoided, but 2d makes my example easier to explain.
if we had the following tokens mapped in 2d space
Apple 1a
Pear 1b
Donkey 2a
Horse 2b
I wonder if a future, vastly more competent AI overseeing its own training might use this form of ambiguity as means to create a model in which the weights are selected so the potential for encoding collisions exist, so that it is possible to deceive an mech.int. observer as to the true thoughts of the model, essentially enabling the ai to think in euphemisms?
PaulPauls, how would you like us to cite your work?
You mentioned you spent your own time and money on it, would you be willing to share how much you spent? It would help others who might be considering independent research.