Lossy and lossless are way more transferable than people give credit.
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.
If you've read the article, the LLM hallucinations aren't due to the model not knowing the information but a function that choose to remember the wrong thing.
> 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.
