4 ms·

the chain of thought is what it is thinking

• ursAxZA 9mo ago
  Chain-of-thought is a technical term in LLMs — not literally “what it’s thinking.” As far as I understand it, it’s a generated narration conditioned by the prompt, not direct access to internal reasoning.
• Bjartr 9mo ago
  It is text that describes a plausible/likely thought process that conditions future generation by it's presence in the context.
  • CamperBob2 9mo ago
    Interestingly, it doesn't always condition the final output. When playing with DeepSeek, for example, it's common to see the CoT arrive at a correct answer that the final answer doesn't reflect, and even vice versa, where a chain of faulty reasoning somehow yields the right final answer. It almost seems that the purpose of the CoT tokens in a transformer network is to act as a computational substrate of sorts. The exact choice of tokens may not be as important as it looks, but it's important that they are present.
    • Workaccount2 9mo ago
      IIRC Anthropic has research finding CoT can sometimes be uncorrelated with the final output.
    • nowittyusername 9mo ago
      That phenomenon and others is what made it obvious that COT is not its "thinking". I think COT is a process by which the llm expands its processing boundary, in that it allows it to sample over a larger space of possibilities. So its kind of acts like a "trigger" of sorts that allows the model to explore in more ways then without COT. First time I saw this was when I witnessed the "wait" phenomenon. Simply inducing the model to say "wait" in its response improved accuracy of results. as now the model double checked its "work". funny enough it also sometimes lead it to produce a wrong answer where otherwise it should have stuck to its guns. But overall that little wait had a net positive affect. Thats when i knew COT was not same as human thinking as we dont care about trigger words or anything like that, our thinking requires zero language (though it does benefit from language) its a deeper process. Thats why i was interested in latent processing models and foray in that matter.
• arthurcolle 9mo ago
  Wrong to the point of being misleading. This is a goal, not an assumption Source: all of mechinterp
• skissane 9mo ago
  When we think, our thoughts are composed of both nonverbal cognitive processes (we have access to their outputs, but generally lack introspective awareness of their inner workings), and verbalised thoughts (whether the “voice in your head” or actually spoken as “thinking out loud”). Of course, there are no doubt significant differences between whatever LLMs are doing and whatever humans are doing when they “think” - but maybe they aren’t quite as dissimilar as many argue? In both cases, there is a mutual/circular relationship between a verbalised process and a nonverbal one (in the LLM case, the inner representations of the model)
  • ursAxZA 9mo ago
    The analogy breaks at the learning boundary. Humans can refine internal models from their own verbalised thoughts; LLMs cannot. Self-generated text is not an input-strengthening signal for current architectures. Training on a model’s own outputs produces distributional drift and mode collapse, not refinement. Equating CoT with “inner speech” implicitly assumes a safe self-training loop that today’s systems simply don’t have. CoT is a prompted, supervised artifact — not an introspective substrate.
    • skissane 9mo ago
      Models have some limited means of refinement available to themselves already: augment a model with any form of external memory, and it can learn by writing to its memory and then reading relevant parts of that accumulated knowledge back in the future. Of course, this is a lot more rigid than what biological brains can do, but it isn’t nothing. Does “distributional drift and mode collapse” still happen if the outputs are filtered with respect to some external ground truth - e.g. human preferences, or even (in certain restricted domains such as coding) automated evaluations?
      • ursAxZA 9mo ago
        I wasn’t talking about human reinforcement. The discussion has been about CoT in LLMs, so I’ve been referring to the model in isolation from the start. Here’s how I currently understand the structure of the thread (apologies if I’ve misread anything): “Is CoT actually thinking?” (my earlier comment) → “Yes, it is thinking.” → “It might be thinking.” → “Under that analogy, self-training on its own CoT should work — but empirically it doesn’t.” → “Maybe it would work if you add external memory with human or automated filtering?” Regarding external memory: without an external supervisor, whatever gets written into that memory is still the model’s own self-generated output — which brings us back to the original problem.
• jablongo 9mo ago
  It is what it is thinking consciously / its internal narrative. For example a supervillain's internal narrative with their plans would go into their COT notepad. If we want to really lean into the analogy between human psychology and LLMs. The "internal reasoning" that people keep referencing in this thread.. referring to the transformer weights and inscrutable inner working of a GPT.. isn't reasoning, but more like instinct, or the subconscious.
  • canjobear 9mo ago
    It’s more like if the supervillain had to write one word of his chain of thought, then go away and forget what he was thinking, then come back and write one more word based on what he had written so far, repeating the process until the whole chain of thought is written out. Each token is generated conditional only on the previous tokens.
• catigula 9mo ago
  this is not correct