6 ms·

Are LLMs stackable? If they keep misunderstanding each other, it'll look more like successive applications of JPEG compression.

• ACCount37 1y ago
  By all accounts, yes. "Model collapse" is a popular idea among the people who know nothing about AI, but it doesn't seem to be happening in real world. Dataset quality estimation shows no data quality drop over time, despite the estimates of "AI contamination" trickling up over time. Some data quality estimates show weak inverse effects (dataset quality is rising over time a little?), which is a mindfuck. The performance of frontier AI systems also keeps improving, which is entirely expected. So does price-performance. One of the most "automation-relevant" performance metrics is "ability to complete long tasks", and that shows vaguely exponential growth.
  • grim_io 1y ago
    The jpeg compression argument is still valid. It's lossy compression at the core.
    • ACCount37 1y ago
      I don't think it is. Sure, you can view an LLM as a lossy compression of its dataset. But people who make the comparison are either trying to imply a fundamental deficiency, a performance ceiling, or trying to link it to information theory. And frankly, I don't see a lot of those "hardcore information theory in application to modern ML" discussions around. The "fundamental deficiency/performance ceiling" argument I don't buy at all. We already know that LLMs use high level abstractions to process data - very much unlike traditional compression algorithms. And we already know how to use tricks like RL to teach a model tricks that its dataset doesn't - which is where an awful lot of recent performance improvements is coming from.
      • grim_io 1y ago
        Sure, you can upscale a badly compressed jpeg using ai into something better looking. Often the results will be great. Sometimes the hallucinated details will not match the expectations. I think this applies fundamentally to all of the LLM applications.
        • muldvarp 1y ago
          And if you get that "sometimes" down to "rarely" and then "very rarely" you can replace a lot of expensive and inflexible humans with cheap and infinitely flexible computers. That's pretty much what we're experiencing currently. Two years ago code generation by LLMs was usually horrible. Now it's generally pretty good.
          • grim_io 1y ago
            I think you are selling yourself short if you believe you can be replaced by a next token predictor :)
            • ACCount37 1y ago
              I think humans who think they can't be replaced by a next token predictor think too highly of themselves. LLMs show it plain and clear: there's no magic in human intelligence. Abstract thinking is nothing but fancy computation. It can be implemented in math and executed on a GPU.
              • anthem2025 1y ago
                LLMs have no ability to reason whatsoever. They do have the ability to fool people and exacerbate or cause mental problems.
                • muldvarp 1y ago
                  LLMs are actually pretty good at reasoning. They don't need to be perfect, humans aren't either.
    • elif 1y ago
      In 2025 you can add quality to jpegs. Your phone does it and you don't even notice. So the rhetorical metaphor employed holds up, in that AI is rapidly changing the fundamentals of how technology functions beyond our capacity to anticipate or keep up with it.
      • lm28469 1y ago
        > add quality to jpegs Define "quality", you can make an image subjectively more visually pleasing but you can't recover data that wasn't there in the first place
        • oldpersonintx2 1y ago
          You can if you know what to fill from other sources. Like, the grill of a car. If we know the make and year, we can add detail with each zoom by filling in from external sources
          • hcs 1y ago
            This is an especially bad example, a nice shiny grille is going to be strongly reflecting stuff that isn't already part of the image (and likely isn't covered well by adjacent pixels due the angle doubling of reflection).
      • johnnienaked 1y ago
        Is this like how crypto changed finance and currency
  • Aloisius 1y ago
    Given the number of academic papers about it, model collapse is a popular idea among the people who know a lot about AI as well. Model collapse is something demonstrated when models are recursively trained largely or entirely on their own output. Given most training data is still generated or edited by humans or synthetic, I'm not entirely certain why one would expect to see evidence of model collapse happening right now, but to dismiss it as something that can't happen in the real world seems a bit premature.
    • ACCount37 1y ago
      We've found in what conditions does model collapse happen slower or fails to happen altogether. Basically all of them are met in real world datasets. I do not expect that to change.