4 ms·

Is it really a train of thought if the LLM completely loses tokens as soon as they are trimmed from the context?

• jameshart 3y ago
  Do you think humans have infinite capacity to store their trains of thought? We invented writing stuff down for a reason. Mathematical notation is precisely a tool for enabling humans to extend their train of thought beyond their immediate 'prompt context length'. Even with finite working memory, humans are generally considered capable of exhibiting intelligence.
  • skepticATX 3y ago
    I’d argue that humans have quite a large prompt context when you consider that we build efficient knowledge representations as we process input. I can read a 1000 page book and keep all of it in my “prompt context” even though I’m not memorizing every word that I read. There is no reason that this couldn’t be done with LLMs, but it’s certainly not what they’re doing today - and it puts you squarely back in the realm of old school AI.
    • jameshart 3y ago
      Sure, but that's an argument about scale not about fundamental limitations of the architecture. The OP is saying the LLM approach can't be intelligent. Not even if you add more parameters, more RAM, more scale. You're saying that an LLM might be able to do what you can do if we make the matrices bigger. That's a different claim. Of course, the real trick, and I think it's clear this is the missing step, is that LLM training can read a thousand page book and embed that knowledge into the weights. So what you want to do is take some of the prompt input, and use it to update the weights in the model (or the LORA, or the fine-tuning, or whatever), not just the context. That's not how LLMs are working today. It's not clear to me that we're so many steps away from that being something we can do though.