It sounds like you're asking whether the output of a neural network is a deterministic function of its input. For many LLMs, you can make that answer yes with the right combination of parameters (temperature = 0) and underlying compute (variance in floating point calculations can still introduce randomness in model outputs even when the model should theoretically return the same answer every time).
There are some ways to introduce stochasticity:
1. Add randomness. The temperature or "creativity" hyperparameter in most LLMs does this, as do some decoders. The hardware these models run can also add randomness.
2. Add some concept of state. RNNs do this, some of the approaches which give the LLM a scratch pad or external memory do this, and continuous pre-training sort of does this.
How this affects people's perception of LLMs as thinking machines, I don't know. What if someone took every response I ever gave to every question that was ever asked of me in my life and made a Chinese Room[1] version of me? A lookup table that is functionally identical to my entire existence. In what contexts is the difference meaningful?
[1] https://en.wikipedia.org/wiki/Chinese_room
There are some ways to introduce stochasticity:
1. Add randomness. The temperature or "creativity" hyperparameter in most LLMs does this, as do some decoders. The hardware these models run can also add randomness.
2. Add some concept of state. RNNs do this, some of the approaches which give the LLM a scratch pad or external memory do this, and continuous pre-training sort of does this.
How this affects people's perception of LLMs as thinking machines, I don't know. What if someone took every response I ever gave to every question that was ever asked of me in my life and made a Chinese Room[1] version of me? A lookup table that is functionally identical to my entire existence. In what contexts is the difference meaningful?
[1] https://en.wikipedia.org/wiki/Chinese_room