> This compression is lossy Is compression really lossy? What is an example of lost knowledge?
LLM-Deflate: Extracting LLMs into Datasets
11–20 of 41 posts
Re: LLM-Deflate: Extracting LLMs into Datasets
#12> This compression is lossy Is compression really lossy? What is an example of lost knowledge?
Think about all the times in llm gets it wrong, the fact that would have helped to get it right is something that was lost. I suppose this isn't proof it's lossy just maybe we don't know how to get the data out. Or look at it another way LLMs or just text prediction machines, whatever information doesn't help them predict the next token or conflicts with the likelihood of the next token is something that gets dropped…
I think extraction from the model itself is a bad idea. But extraction from external sources, such as the deep research reports LLMs generate, or solving problems where we have validation of correctness is a good idea. The model is not validating its outputs by simply doing another inference, but consults external sources or gets feedback from code execution. Humans in chat rooms could also provide lots of learning signal, especially when actions are judged against the outcomes they cause down the line, using hindsight.
So in short what works is a model + a way to know its good outputs from bad ones.
Re: LLM-Deflate: Extracting LLMs into Datasets
#13Wouldn’t this method be good if applied on humans in job interviews?
Re: LLM-Deflate: Extracting LLMs into Datasets
#14> This compression is lossy Is compression really lossy? What is an example of lost knowledge?
Think about all the times in llm gets it wrong, the fact that would have helped to get it right is something that was lost. I suppose this isn't proof it's lossy just maybe we don't know how to get the data out. Or look at it another way LLMs or just text prediction machines, whatever information doesn't help them predict the next token or conflicts with the likelihood of the next token is something that gets dropped…
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Re: LLM-Deflate: Extracting LLMs into Datasets
#15My understanding was that the Alpaca data was a distillation from text-davinci-003
Re: LLM-Deflate: Extracting LLMs into Datasets
#16My gripe with an approach like this is the lack of any grounding to these generated topics. Hallucination accumulates like error in this case so every generation that is conditioned by a previous one (the recursive "hierarchical topic exploration" in TFA). I suspect most of the "leafs" are unusable.
The question is: Is it like jpeg compression where the errors do not accumulate but the image comverges to a self inverse compressed image or does the data set converge to a single point which is meaningless?
Re: LLM-Deflate: Extracting LLMs into Datasets
#17Earlier quoted context omitted.
it's exactly the same as JPEG images being lossy, while you can see image as a whole (and it is enough for 99% of people), you are obviously missing some details and the more you rely on those details (professional photography, scientific data) the more obvious it is (to the point of image being useless in some cases) same with LLMs, we are currently testing how far we can go before we seeing obvious issues
whats an example of loss?
Lossy compression vs lossless compression is the difference of whether you can get a 1:1 copy of the original data if you compress and then decompress it.
A simple example of this is if you have 4 bits of data and have a compression algorithm that turns it into 2 bits of data. If your dataset only contains 0000, 0011, 1100, and 1111; then this can technically be considered lossless compression because we can always reconstruct the exact original data (e.g. 0011 compresses to 01 and can decompress back to 0011, 1100 compresses to 10 and can decompress back to 1100, etc). However, if our dataset later included 1101 and got compressed to 10, this is now “lossy” because it would decompress to 1100, that last bit was “lost”.
An LLM is lossy compression because it lacks the capacity to 1:1 replicate all its input data 100% of the time. It can get quite close in some cases, sure, but it is not perfect every time. So it is considered “lossy”.
Re: LLM-Deflate: Extracting LLMs into Datasets
#18Wouldn’t this method be good if applied on humans in job interviews?
how long would it take to do a complete memory dump of your brain by voice stream? days? months? years?
this is more like writing one's autobiography.
Re: LLM-Deflate: Extracting LLMs into Datasets
#19> This compression is lossy Is compression really lossy? What is an example of lost knowledge?
It is at least as lossy as jpeg compression. Details get lost and artifacts are generated.
Re: LLM-Deflate: Extracting LLMs into Datasets
#20Earlier quoted context omitted.
it's exactly the same as JPEG images being lossy, while you can see image as a whole (and it is enough for 99% of people), you are obviously missing some details and the more you rely on those details (professional photography, scientific data) the more obvious it is (to the point of image being useless in some cases) same with LLMs, we are currently testing how far we can go before we seeing obvious issues
whats an example of loss?
How good can you recreate an image that is described by words? Obviously not bit by bit and pixel by pixel. You get something that resembles the original but not an exact copy.