It'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.
replyRight, no hidden internal state. Exactly. There's 0. And the weights are sitting there statically, which is absolutely true.
replyBut 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)
It’s a completely different substrate. LLMs don’t have agency, they don’t have a conscious, they don’t have experiences, they don’t learn over time. I’m not saying that the debate is closed, but I also think there is great danger in thinking because a machine produces human-like output, that it should be given human-like ethical considerations. Maybe in the future AI will be considered along those grounds, but…well, it’s a difficult question. Extremely.
replyWhat's the empirical basis for each of your statements here? Can you enumerate? Can you provide an operational definition for each?
replyCommon sense.
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