Comment by jermaustin1
10 hours ago
I find the same issues (though with much lower stakes) when using an LLM to determine the outcome of a turn in a game. I'm working on something called "A Trolly (problem) Through Time" where each turn is a decade starting with the 1850s, and you are presented with historic figures on a train track, and you have to chose whether to actively spare the person on your track for a potential unknown figure on the other side, or let the train run them over.
It works well as a narrative, but the second I started adding things like tracking high level macro effects of the decisions, within a couple of turns the world's "Turmoil" goes from 4/10 to a 10/10... even when the person that was killed would have been killed IRL.
Sonnet 4, o4-mini, and GPT 4o-mini all had the same world ending outcomes not matter who you kill. Killing Hitler in 1930s: 10/10 turmoil, Killing Lincoln in the 1850s: 10/10 turmoil in the first turn.
I've come to the realization, the LLM shouldn't be used for the logic, and instead needs to be used to just narrate the choices you make.
"I've come to the realization, the LLM shouldn't be used for the logic, and instead needs to be used to just narrate the choices you make."
This exactly right. LLMs are awesome for user<>machine communication, but are still painful to try to use as a replacement for the machine itself.
I wonder if this is due to the common trope in science fiction literature that changing the past in even a small way has a butterfly effect of unintended and frequently disastrous consequences.