Thanks! I’m inclined to broadly agree, and I like this as a working definition. That said I’ll note that it’s important to avoid making a false equivalence fallacy—the connection between ‘latent variables that define a unique context in which a document was generated’ and ‘attributes that shape models’ goals, beliefs, values, behaviour etc’ feels true-ish but not fully fleshed out at the moment.
Also, looking back on my definition, I’m willing to use ‘persona’ to describe the aggregated editors of a wikipedia page, or the output of another LLM acting agentically, however for something sufficiently non-agentic like tokens from an automated weather report station, that term seems a bit too humanlike and agentic. At some point this starts being, still a part of the world model, but one that has nothing to do with agentic/human-like behavior, and then applying an anthropomorphically loaded term like ‘persona’ to that seems unjustified. How about this:
Out of the distribution of meaningfully distinct (i.e. perplexity-reducing) token-generation contexts / processes found in the training material (principally pretraining, supplemented by later training), consider the subset that are meaningfully agentic/humanlike, and then consider the properties of them that the word ‘persona’ would include for a human or a fictional character.
As for the equivalence, it’s strongly suspected (but not yet proven) that SGD is an approximation to Bayesian learning. The former is the input to the Bayesian learning process, the latter the output. Obviously there are capacity limitations. I’m working on a past on Simulator Theory that will go into this in more detail.
Thanks! I’m inclined to broadly agree, and I like this as a working definition. That said I’ll note that it’s important to avoid making a false equivalence fallacy—the connection between ‘latent variables that define a unique context in which a document was generated’ and ‘attributes that shape models’ goals, beliefs, values, behaviour etc’ feels true-ish but not fully fleshed out at the moment.
Also, looking back on my definition, I’m willing to use ‘persona’ to describe the aggregated editors of a wikipedia page, or the output of another LLM acting agentically, however for something sufficiently non-agentic like tokens from an automated weather report station, that term seems a bit too humanlike and agentic. At some point this starts being, still a part of the world model, but one that has nothing to do with agentic/human-like behavior, and then applying an anthropomorphically loaded term like ‘persona’ to that seems unjustified. How about this:
Out of the distribution of meaningfully distinct (i.e. perplexity-reducing) token-generation contexts / processes found in the training material (principally pretraining, supplemented by later training), consider the subset that are meaningfully agentic/humanlike, and then consider the properties of them that the word ‘persona’ would include for a human or a fictional character.
As for the equivalence, it’s strongly suspected (but not yet proven) that SGD is an approximation to Bayesian learning. The former is the input to the Bayesian learning process, the latter the output. Obviously there are capacity limitations. I’m working on a past on Simulator Theory that will go into this in more detail.