61–70 of 117 posts
As I am going through the article, I was wondering why is this more interesting than having the ability to recover java programs from byte code. So I asked copilot the same question. It told me that - "Honestly this is where the difference between an engineer and researcher shows up!" .
The big questions I’m taking away are: (1) they are claiming to produce apparently bijective closed-form symbolic representations/approximations of, among other things, LLMs. Is evaluating these closed-form representations more computationally efficient? The implications of that are potentially huge. It would be essentially analytic distillation. Fable on a chip and not a data center would be important — and disrupti…
> "Second, there is no guarantee that a given neural network can be approximated by DISCOVER"
They train what appears as embeddings for outer product of roles and fillers. The role for language model can be a position in text, the filler can be an embedding of a word at that position. Then that matrix of a sum of these outer products is linearly mapped into NN encodings and then decoded by NN decoder.
The embeddings learned by this process are not necessarily smaller than original ones. Given that they participate in an outer product computation gives me impression that the resulting sum is much bigger than actual NN encoding, that is why it needs to be linearly mapped into NN encoding.
So, this paper will not necessarily lead to any computation savings.
But I am at page 6. ;)
The big questions I’m taking away are: (1) they are claiming to produce apparently bijective closed-form symbolic representations/approximations of, among other things, LLMs. Is evaluating these closed-form representations more computationally efficient? The implications of that are potentially huge. It would be essentially analytic distillation. Fable on a chip and not a data center would be important — and disrupti…
No it is way worse since computationally they are same things with symbolic having overhead of attending to the symbols.
As I am going through the article, I was wondering why is this more interesting than having the ability to recover java programs from byte code. So I asked copilot the same question. It told me that - "Honestly this is where the difference between an engineer and researcher shows up!" .
as long as it is honest, everything is ok.
The big questions I’m taking away are: (1) they are claiming to produce apparently bijective closed-form symbolic representations/approximations of, among other things, LLMs. Is evaluating these closed-form representations more computationally efficient? The implications of that are potentially huge. It would be essentially analytic distillation. Fable on a chip and not a data center would be important — and disrupti…
> Is evaluating these closed-form representations more computationally efficient? No it is way worse since computationally they are same things with symbolic having overhead of attending to the symbols.
As I am going through the article, I was wondering why is this more interesting than having the ability to recover java programs from byte code. So I asked copilot the same question. It told me that - "Honestly this is where the difference between an engineer and researcher shows up!" .
Earlier quoted context omitted.
It certainly comes across as disrespectful. To my reading the nature of supervision creating the structure seems to be the core of the argument.
I have issue with the comment because he says he didn't read it, then unfavorably compares it to a previous method, and finally uses that negative review to plug his own article instead. His criticism might be valid, I'm not in a position to judge, but the self-promotion leaves a sour tastes in my mouth and makes me question how much of the criticism is just drummed up to make his own contribution appear more relevan…
Earlier quoted context omitted.
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.)
Reading stuff like this (as a layman), diminishing these things as 'Next token predictors' seems absurdly reductive. At some point we'll need to concede that 'selection' is a better term for this than prediction.
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.