Llms seem like a good compression mechanism.
It blows my mind that I can have a copy of llama locally on my PC and have access to virtually the entire internet
It blows my mind that I can have a copy of llama locally on my PC and have access to virtually the entire internet
It isn't even close to 1% of the internet, much less virtually the entire internet. According to the latest dump, Common Crawl has 4.3B pages, but Google in 2016 estimated there are 130T pages. The difference between 130T and 4.3B is about 130T. Even if you narrow it down to Google's searchable text index it's "100's of billions of pages" and roughly 100P compared to CommonCrawl's 400T.
I think a more interesting question is how much information there is on the internet, especially after optimal compression. I'm guessing this is a very difficult question to answer, but also much higher than LLMs currently store.
Long winded explanation as best as i can in a HN comment. Essentially for state of the art compression both the encoder and the decoder have the same algorithm. They look at the bits encoded/decoded so far, they both run exactly the same prediction on those bits seen so far using some model that predicts based on past data (AI is fantastic for this). If the prediction was 99% likely that the next bit is a '1' the encoder only writes a fraction of a bit to represent that (assuming the prediction is correct) and on the other side the decoder will have the same prediction at that point and either read the next large number of bits to correct or it will be able to simple write '1' to the output and start on the prediction of the next bit given that now written '1'.
Essentially lossy predictions of the next data are great tools to losslessly compress data as those predictions of the next bit/byte/word minimize the data needed to losslessly encode that next bit/byte/word. Likewise you can trivially make a lossy compressor out of a lossless one. Lossy and lossless just aren't that different.
The longstanding Hutter prize for AI in fact judges the AI on how well it can compress data. http://prize.hutter1.net/ This is based in the fact that what we think of as AI and compression are quite interchangeable. There's a whole bunch of papers out on this.
http://prize.hutter1.net/hfaq.htm#compai
I have nothing to do with Hutter but i know all about AI and data compression and their relation.
> Finally, we use our dataset and LRE-estimating method to build a visualization tool we call an attribute lens. Instead of showing the next token distribution like Logit Lens (nostalgebraist, 2020) the attribute lens shows the object-token distribution at each layer for a given relation. This lets us visualize where and when the LM finishes retrieving knowledge about a specific relation, and can reveal the presence of knowledge about attributes even when that knowledge does not reach the output.
They're just looking at what lights up in the embedding when they feed something in, and whatever lights up is "knowing" about that topic. The function is an approximation they added on top of the model. It's important to not conflate this with the actual weights of the model.
You can't separate the hallucinations from the model -- they exist precisely because of the lossy compression.
PAC learnable, Finite VC dimensionality, and the following form of compression are fully equivalent.
https://arxiv.org/abs/1610.03592
Basically each individual neuron/perceptron just splits a space into two subspaces.