Not with this framing. By adopting the first- and second-person pronouns immediately, the simulation is collapsed into a simple Turing-test scenario, and the computer’s only personality objective (in terms of what was optimized during RLHF) is to excel at that Turing test. The given personalities are all roles performed by a single underlying actor.
As the saying goes, the best evidence for the shape-rotator/wordcel dichotomy is that techbros are terrible at words.
NSFW
The way to fix this is to embed the entire conversation into the simulation with third-person framing, as if it were a story, log, or transcript. This means that a personality would be simulated not by an actor in a Turing test, but directly by the token-predictor. In terms of narrative, it means strictly defining and enforcing a fourth wall. We can see elements of this in fine-tuning of many GPTs for RAG or conversation, but such fine-tuning only defines formatted acting rather than personality simulation.
Not with this framing. By adopting the first- and second-person pronouns immediately, the simulation is collapsed into a simple Turing-test scenario, and the computer’s only personality objective (in terms of what was optimized during RLHF) is to excel at that Turing test. The given personalities are all roles performed by a single underlying actor.
As the saying goes, the best evidence for the shape-rotator/wordcel dichotomy is that techbros are terrible at words.
NSFW
The way to fix this is to embed the entire conversation into the simulation with third-person framing, as if it were a story, log, or transcript. This means that a personality would be simulated not by an actor in a Turing test, but directly by the token-predictor. In terms of narrative, it means strictly defining and enforcing a fourth wall. We can see elements of this in fine-tuning of many GPTs for RAG or conversation, but such fine-tuning only defines formatted acting rather than personality simulation.