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Lossy compression does make things up. We call them compression artefacts. In compressed audio these can be things like clicks and boings and echoes and pre-ec

• jpcompartir 1y ago
  Interesting, in the LLM case these compression artefacts then get fed into the generating process of the next token, hence the errors compound.
  • ACCount37 1y ago
    Not really. The whole "inference errors will always compound" idea was popular in GPT-3.5 days, and it seems like a lot of people just never updated their knowledge since. It was quickly discovered that LLMs are capable of re-checking their own solutions if prompted - and, with the right prompts, are capable of spotting and correcting their own errors at a significantly-greater-than-chance rate. They just don't do it unprompted. Eventually, it was found that reasoning RLVR consistently gets LLMs to check themselves and backtrack. It was also confirmed that this latent "error detection and correction" capability is present even at base model level, but is almost never exposed - not in base models and not in non-reasoning instruct-tuned LLMs. The hypothesis I subscribe to is that any LLM has a strong "character self-consistency drive". This makes it reluctant to say "wait, no, maybe I was wrong just now", even if latent awareness of "past reasoning look sketchy as fuck" is already present within the LLM. Reasoning RLVR encourages going against that drive and utilizing those latent error-correction capabilities.
    • Mallowram 1y ago
      The problem is that language doesn't produce itself. Re-checking, correcting error is not relevant. Error minimization is not the fount of survival, remaining variable for tasks is. The lossy encyclopedia is neither here nor there, it's a mistaken path: "Language, Halliday argues, "cannot be equated with 'the set of all grammatical sentences', whether that set is conceived of as finite or infinite". He rejects the use of formal logic in linguistic theories as "irrelevant to the understanding of language" and the use of such approaches as "disastrous for linguistics"."
      • ACCount37 1y ago
        Sorry, what? This is borderline incoherent.
        • mallowdram 1y ago
          The units themselves are meaningless without context. The point of existence, action, tasks is to solve the arbitrariness in language. Tasks refute language, not the other way around. This may be incoherent as the explanation is scientific, based in the latest conceptualization of linguistics. CS never solved the incoherence of language, conduit metaphor paradox. It's stuck behind language's bottleneck, and it do so willingly blind-eyed.
          • ACCount37 1y ago
            What? This is even less coherent. You weren't talking to GPT-4o about philosophy recently, were you?
            • mallowdram 1y ago
              I'd know cutting-edge linguistics and signaling theory well beyond Shannon to parse this, not NLP or engineering reduction. What I've stated is extremely coherent to Systemic Functional Linguists. Beyond this point engineers actually have to know what signaling is, rather than 'information.' https://www.sciencedirect.com/science/article/abs/pii/S0003347209002589 https://www.sciencedirect.com/science/article/abs/pii/S00033... Ultimately, engineering chose the wrong approach to automating language, and it sinks the field. It's irreversible.
• latexr 1y ago
  I feel like my comment is pretty clear that a compression artefact is not the same thing as making the whole thing up. > Of course the analogy isn't exact. And I don’t expect it to be, which is something I’ve made clear several times before, including on this very thread. https://news.ycombinator.com/item?id=45101679 https://news.ycombinator.com/item?id=45101679
  • tsunamifury 1y ago
    More disagreeing with no meaningful value to the conversation. This is you. Constantly.
• moregrist 1y ago
  > 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.
  • baq 1y ago
    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.
  • Vetch 1y ago
    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.