alt Hacker NewsIt's plausible that LLMs experience things during training, but during inference an LLM is equivalent to a lookup table. An LLM is a pure function mapping a list of tokens to a set of token probabilities. It needs to be connected to a sampler to make it "chat", and each token of that chat is calculated separately (barring caching, which is an implementation detail that only affects performance). There is no internal state.
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Right, no hidden internal state. Exactly. There's 0. And the weights are sitting there statically, which is absolutely true.
But my current favorite frontier model has this 1 million token mutable state just sitting there. Holding natural language. Which as we know can encode emotions. (Which I imagine you might demonstrate on reading my words, and then wisely temper in your reply)
The context is state. This is especially noticable for thinking models, which can emit tens of thousands of CoT tokens solving a problem. I'm guessing you're arguing that since LLMs "experience time discretely" (from every pass exactly one token is sampled, which gets appended to the current context), they can't have experiences. I don't think this argument holds - for example, it would mean a simulated human brain may or may not have experiences depending on technical details of how you simulate them, even though those ways produce exactly the same simulation.