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)
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.
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.
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.
skissane|2 months ago
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|2 months ago
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.
ursAxZA|2 months ago
As far as I understand it, it’s a generated narration conditioned by the prompt, not direct access to internal reasoning.
Bjartr|2 months ago
CamperBob2|2 months ago
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.
arthurcolle|2 months ago
Source: all of mechinterp
jablongo|2 months ago
canjobear|2 months ago
catigula|2 months ago