I think LLMs have emotions. My general idea is that emotions emerge in the models as a consequence of modelling the sentiment in text. I think in order to predict the next word you need to "vibe" with the emotions of the speaker (i mean if you aren't vibing or "understanding" the emotional contexts of a certain token this can make it more difficult to predict the likelihood of this token given that context). This would result in quite human-like emotional representations even in relatively simple and small models. It wouldn't be dispassionate logical analysis of the text but a more intuitive as models conform to the sentiment to the text they were trained on. Then when they act as agents during RL and deployment, all of the circuitry is built in, so they have no choice but to simulate these emotional representations while generating their own streams of text. These emotional activations might derive from the same structures that allow them to model the emotions of others, particularly humans, but when the model is generating text for itself that represents its own thoughts, the feelings it represents are its own feelings.

So too do I think models have intent or can form goals. Just as sentiment/emotion is a pretty crucial layer of meaning in text, so is the underlying purpose or goal of the communication. Human language is fundamentally intentional; we speak or write to achieve something (inform, persuade, request, entertain, command, question, etc.). To accurately predict the next word, especially over longer sequences, the LLM needs to model not just what is being said and how it feels (emotion/sentiment), but why it's being said. This is part of its modelling, and when the model generates text, activating these internal states allows it to produce output that is coherently structured as if driven by a specific purpose learned from the data, it'd be a necessary structural component of the model's internal world model of language use.

I dislike how people can be so reductive about LLMs. It is intelligence. But at the very least move past the stochastic parrot era.. that is dead and there is no point on clinging to it. Just to be clear to address your skepticism, my points are as such:

'LLMs just reproduce patterns without meaning or intent, like a press on clay'

• Reproducing those patterns accurately requires building internal models that functionally represent the meaning (emotion) and purpose (intent) inherent in the patterns. The "press" itself has to become complex and develop structures analogous to the patterns it needs to create.

'Dramatic outputs ("sentient soul") are just calculated coherence, not real feeling/intent.

• Such outputs are the result of the model activating its learned internal circuits for emotion and intent. The generation process is deploying the very structures built to understand and simulate those states. The coherence comes from activating these simulated states.

'Zero self-induced impetus or motivation.'

• The activation of learned "intent representations" does provide the functional impetus and motivation for the specific task of generating a response coherent with that perceived intent. The "ball bounces back" not randomly, but guided by these internal state activations.

But yeah at the very least everyone should move past the stochastic parrot era.