Comment by moregrist | Hacker News Reader

Comment by moregrist | Hacker News Reader

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> Lossy compression does make things up. We call them compression artefacts.

I don’t think this is a great analogy.

Lossy compression of images or signals tends to throw out information based on how humans perceive it, focusing on the most important perceptual parts and discarding the less important parts. For example, JPEG essentially removes high frequency components from an image because more information is present with the low frequency parts. Similarly, POTS phone encoding and mp3 both compress audio signals based on how humans perceive audio frequency.

The perceived degradation of most lossy compression is gradual with the amount of compression and not typically what someone means when they say “make things up.”

LLM hallucinations aren’t gradual and the compression doesn’t seem to follow human perception.

You are right and the idea of LLMs as lossy compression has lots of problems in general (LLMs are a statistical model, a function approximating the data generating process).

Compression artifacts (which are deterministic distortions in reconstruction) are not the same as hallucinations (plausible samples from a generative model; even when greedy, this is still sampling from the conditional distribution). A better identification is with super-resolution. If we use a generative model, the result will be clearer than a normal blotchy resize but a lot of details about the image will have changed as the model provides its best guesses at what the missing information could have been. LLMs aren't meant to reconstruct a source even though we can attempt to sample their distribution for snippets that are reasonable facsimiles from the original data.

An LLM provides a way to compute the probability of given strings. Once paired with entropy coding, on-line learning on the target data allows us to arrive at the correct MDL based lossless compression view of LLMs.

LLM confabulations might as well be gradual in the latent space. I don’t think lossy is synonymous to perceptual and the high frequency components rather easily translate to less popular data.

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