[HN Gopher] Logit Prisms: Decomposing Transformer Outputs for Me...
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Logit Prisms: Decomposing Transformer Outputs for Mechanistic
Interpretability
Author : xcodevn
Score : 38 points
Date : 2024-06-17 11:57 UTC (2 days ago)
(HTM) web link (neuralblog.github.io)
(TXT) w3m dump (neuralblog.github.io)
| behnamoh wrote:
| Off topic: can anyone tell me what the author has used to convert
| their paper into this beautiful webpage? It's apparently a trend
| with ML papers to have their own website.
| nbgoodall wrote:
| From the source, looks like they're using Quarto:
| https://quarto.org
| behnamoh wrote:
| Thank you!
| esafak wrote:
| A popular alternative is https://distill.pub/guide/
| xcodevn wrote:
| If you're reading this, you may be interested in my other work on
| "Exploring Llama-3 MLP Neurons" as well.
|
| https://neuralblog.github.io/llama3-neurons/
| nico wrote:
| Tangent, the screen reader recording is cool. The main issue for
| me is that it doesn't know where I am on the page, so it just
| starts from the beginning and I need to guess the position of the
| relevant audio based on how far down on the page I am. It would
| be cool to have some sort of bookmarks that match both the text
| and the audio, so that I can skip both to the same section at the
| same time
| XorNot wrote:
| Okay so what stood out to me here is the math work: namely, if we
| can identify the parts of the model which are doing the math...
| Can that be "augmented" to generalise it? Could you take over the
| math neurons and either optimise them by hand or plug in a
| software "implant" to do the work properly?
| Lerc wrote:
| It will be an area of active research, but it remains to be
| seen if it is overall beneficial.
|
| Custom architecture components break the homogeneity that
| enables the massive parallelism.
|
| There may be an approach similar to some FPGAs where there are
| a few things that are commonly needed that might be implemented
| specifically to avoid constructing them out of generalisable
| parts. The calculation is quite different though because
| hardware has more fixed limits and being fixed once
| manufactured means a great deal more forethought has to take
| place. Software models, being more flexible can avoid the extra
| thinking by adding a few million parameters.
|
| One possibility is KANs which can also provide a calculable
| expression. I could see a fancy convolution KAN being converted
| into an expression and then applied across an entire input.
|
| I feel like maybe in the future there may be a compiler that
| can make custom GPU code for specific pathways.
|
| In the short term it might be useful if there is some found
| formula that requires many layers that can be calculated
| earlier with a custom expression and fed directly into a higher
| layer enabling the information to be more broadly used.
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(page generated 2024-06-19 23:01 UTC)