Anthropic has some similar findings, and they propose an architectural change (activation capping) that apparently helps keep the Assistant character away from dark traits (sometimes). But it hasn't been implemented in any models, I assume because of the cost of scaling it up.
this post was submitted on 27 Mar 2026
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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
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