When you talk to a large language model, you can think of yourself as talking to a character
But who exactly is this Assistant? Perhaps surprisingly, even those of us shaping it don’t fully know
Fuck me that’s some terrifying anthropomorphising for a stochastic parrot
The study could also be summarised as “we trained our LLMs on biased data, then honed them to be useful, then chose some human qualities to map models to, and would you believe they align along a spectrum being useful assistants!?”. They built the thing to be that way then are shocked? Who reads this and is impressed besides the people that want another exponential growth investment?
To be fair, I’m only about 1/3rd of the way through and struggling to continue reading it so I haven’t got to the interesting research but the intro is, I think, terrible
How it functionally works, its the next word / token / chunk a lot more than its an “idea”. An idea is even rough to define
The other relatively accurate analogy is a probabilistic database
Neither work if you’ve fallen into anthropomorphising, but they’re relatively accurate to architecture and testing for people that aren’t too computer literate, far more than the anthropomorphising alternatives at least