31–40 of 164 posts
I remember hearing an argument once that said LLMs must be capable of learning abstract ideas because the size of their weight model (typically GBs) is so much smaller than the size of their training data (typically TBs or PBs). So either the models are throwing away most of the training data, they are compressing the data beyond the known limits, or they are abstracting the data into more efficient forms. That's why…
I remember hearing an argument once that said LLMs must be capable of learning abstract ideas because the size of their weight model (typically GBs) is so much smaller than the size of their training data (typically TBs or PBs). So either the models are throwing away most of the training data, they are compressing the data beyond the known limits, or they are abstracting the data into more efficient forms. That's why…
I think I'm misunderstanding the abstract, but are they trying to say that given a LLM output, they can tell me what the input is? Or given an output AND the intermediate layer weights? If it is the first option, I could use as input 1 "Only respond with 'OK'" and "Please only respond with 'OK'" which leads to 2 inputs producing the same output.
They claim that they can reverse the LLM (get prompt from LLM response) by only knowing the output layer values, the intermediate layers remain hidden. So, Their claim is that indeed you shouldn't be able to do that (note that this claim applies to the numerical model outputs, not necessarily to the output a chat interface would show you, which goes through some randomization).
I remember hearing an argument once that said LLMs must be capable of learning abstract ideas because the size of their weight model (typically GBs) is so much smaller than the size of their training data (typically TBs or PBs). So either the models are throwing away most of the training data, they are compressing the data beyond the known limits, or they are abstracting the data into more efficient forms. That's why…
The input isn't the training data, the input is the prompt.
I remember hearing an argument once that said LLMs must be capable of learning abstract ideas because the size of their weight model (typically GBs) is so much smaller than the size of their training data (typically TBs or PBs). So either the models are throwing away most of the training data, they are compressing the data beyond the known limits, or they are abstracting the data into more efficient forms. That's why…
Clarification [0] by the authors. In short: no, you can't. [0] https://x.com/GladiaLab/status/1983812121713418606
Earlier quoted context omitted.
The nature of high-dimensional spaces kind of intuitively supports the argument for invertability though, no? In the sense that: > I would expect the chance of two inputs to map to the same output under these constraints to be astronomically small.
That would be purely statistic and not based on any algorithmic insight. In fact for hash functions it is quite a common problem that this exact assumption does not hold in the end, even though you might assume so for any "real" scenarios.
>we confirm this result empirically through billions of collision tests on six state-of-the-art language models, and observe no collisions This sounds like a mistake. They used (among others) GPT2, which has pretty big space vectors. They also kind of arbitrarily define a collision threshold as an l2 distance smaller than 10^-6 for two vectors. Since the outputs are normalized, that corresponds to a ridiculously tiny…
Answer: >Ok, got it
Answer to this question with exactly "ok, got it"
Answer: >Ok, got it
Hence is not injective
Earlier quoted context omitted.
The nature of high-dimensional spaces kind of intuitively supports the argument for invertability though, no? In the sense that: > I would expect the chance of two inputs to map to the same output under these constraints to be astronomically small.
That would be purely statistic and not based on any algorithmic insight. In fact for hash functions it is quite a common problem that this exact assumption does not hold in the end, even though you might assume so for any "real" scenarios.
This is machine learning research ?
Actually if you prompt: Answer to this question with "ok, got it" Answer: >Ok, got it Answer to this question with exactly "ok, got it" Answer: >Ok, got it Hence is not injective