(Léo Dana) French master student in applied Mathematics (probability & statistic), soon alignment researcher (?)
Quick question: you say that the MLP 2-6 gradually improve the representation of the sport of the athlete, and that no single MLP do it in one go. Would you consider that the reason would be something like this post describes ? https://www.lesswrong.com/posts/8ms977XZ2uJ4LnwSR/decomposing-independent-generalizations-in-neural-networks
So the MLP 2-6 basically do the same computations, but in a different superposition basis so that after several MLPs, the model is pretty confident about the answer ? Then would you think there is something more to say in the way the "basis are arranged", eg which concept interfere with which (i guess this could help answering questions like "how to change the lookup table name-surname-sport" which we are currently not able to do)
Thanks for the post Ellena!
I was wondering if the finding "words are clustered by vocal and semantic similarity" also exists in traditional LLMs? I don't remember seeing that, so could it mean that this modularity could also make interpretability easier?
It seems logical: we have more structure on the data, so better way to cluster the text, but I'm curious of your opinion.
Hi, Interesting experiments.
What were you trying to find and how would you measure that the content is correctly mixed instead of just having "unrealated concepts juxtaposed" ?
Also, how did you choose which layer to merge your streams ?
Hi, thank you for the sequence. Do you know if there is any way to get access the Watanabe’s book for free ?
In a MLP, the nodes from different layers are in Series (you need to go through the first, and then the second), but inside the same layer they are in Parallel (you go through one of the other).
The analogy is with electrical systems, but I was mostly thinking in terms of LLM components: the MLPs and Attentions are in Series (you go through the first and after through the second), but inside one component, they are in parallel.
I guess that then, inside a component there is less superposition (evidence is this post), and between component there is redundancy (so if a computation fails somewhere, it is done also somewhere else).
In general, dropout makes me feel like because some part of the network are not going to work, the network has to implement "independent" component for it to compute thing properly.
One thing I just thought about: I would predict that dropout is reducing superposition in parallel and augment superposition in series (because to make sure that the function is computed, you can have redundancy)
Thank you, Idk why but before I ended up on a different page with broken links (maybe some problem on my part)!
Hey, almost all links are dead, would it be possible to update them ? otherwise the post is pretty useless and I am interested in them ^^
Indeed. D4 is better than D5 if we had to choose, but D4 is harder to formalize.
I think that having a theory of corrigibility without D4 is already something a good step as D4 seems like "asking to create corrigible agent", so you maybe the way to do it is: 1. have a theory of corrigible agent (D1,2,3,5) and 2. have a theory of agent that ensures D4 by apply the previous theory to all agent and subagent.
Thank you! I haven't read Armstrongs' work in detail on my side, but I think that one key difference is that classical indifference methods all try to make the agent "act as if the button could not be pressed" which causes the big gamble problem.
By the way, do you have any idea why almost all link on the page you linked are dead or how to find the mentioned articles ??