111–117 of 117 posts
Instead of distilling the geometry of a model into a huge knowledge graph, we start from the largest known open source graphs and build it up towards something that resembles this geometry.
Come and join us. Discuss on github.com/latentpedia. We have the basic tech covered. Need more compute, storage and enough business to cover the cost of serving.
A big problem with some of these supervised* interpretability approaches is that they can find spurious structure. (There are lots of ways to make the model do what you want; which is roughly what Hewitt and Liang 2019 showed). This paper draws a contrast to a previous method, DAS (distributed alignment search) on page 20. These and related methods rest on theories of causal abstraction, which are great in theory, bu…
So, basically you're saying: I have only skimmed the article, similar approaches had issues in the past, and my own method from my forthcoming paper is better. I mean, no disrespect, but that's the core of your argument, yes?
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I think it would be fair to say that at least a few people can do a small amount of 4d visualisation. Agreed, I don't think anyone has a good grasp on, say, 64K D, but we can do a bit better than just 3.
I’ve heard people claiming that they can but I’ve never heard compelling evidence that they’re directly visualising 4D objects rather than 3D projections of them, or some symbolic representation (arrays of numbers etc.)
I mean, there are people with aphasia and hyperplasia - how do you prove actual 3D visualisation.
There are games that people play that need 4D visualisation to be able to do well and they perform at that.
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You are disconnected from reality. The whole industry is already completely dominated by LLMs generating code. Bury your head in the sand all you want. This is not about low expectations or inexperience at all. Your condescending tone doesn't make you look smarter, it makes you look like an Amish who expects the industrial revolution is temporary and soon people will come to their senses and stop using all this nonse…
A sidenote, but that is not a typical Amish opinion of technology: "Amish do not view technology as evil, and individuals may petition for acceptance of a particular technology in the local community" - https://en.wikipedia.org/wiki/Amish_way_of_life#Use_of_moder... See also https://thereader.mitpress.mit.edu/how-the-amish-let-technol... about some Amish use of cell phones with adapters to be more payphone/landline-l…
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> diminishing these things as 'Next token predictors' seems absurdly reductive. This shows a deep misunderstanding of the paper's claims, which in no way challenge the established view that these bots are next-token predictors. Regardless, if all you want is a next-token selector , save your money and roll a die.
> which in no way challenge the established view that these bots are next-token predictors. I mean, of course they are, that's literally what the inference loop does. You can look at the source of your favorite model runner and you'll see exactly that. What I find misleading about this term is that it focuses attention on the "next token" part and glosses over the "prediction" part as some sort of unspecified "statis…
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> which in no way challenge the established view that these bots are next-token predictors. I mean, of course they are, that's literally what the inference loop does. You can look at the source of your favorite model runner and you'll see exactly that. What I find misleading about this term is that it focuses attention on the "next token" part and glosses over the "prediction" part as some sort of unspecified "statis…
I've seen nothing to suggest it misleads anyone else.
I've seen various posters talk about "stochastic parrots" or about how LLMs were just "using very simple word statistics" to get their results, which sounded to me very much as if they thought of LLMs simply as glorified markov chains. I would consider that a big misunderstanding.
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I've seen nothing to suggest it misleads anyone else.
There are other next token prediction algorithms such as markov chains or HMMs that also "fit the same interface", but are vastly simpler than what LLMs use. I've seen various posters talk about "stochastic parrots" or about how LLMs were just "using very simple word statistics" to get their results, which sounded to me very much as if they thought of LLMs simply as glorified markov chains. I would consider that a bi…
"In machine learning, the term stochastic parrot is a metaphor that frames large language models as systems that statistically mimic text without real understanding." https://en.wikipedia.org/wiki/Stochastic_parrot
"Large Language Models as Markov Chains" https://arxiv.org/abs/2410.02724