In the context of a “war game” this makes sense. If you remain completely neutral it’s impossible to win. Any examples of similar scenarios the model saw during training would have high aggression rates.
Did you read the article? It gave examples of escalations in neutral scenarios that make no sense.
It’s probably vibing on the Dark Forest Theory. If that’s the case, it makes sense to utterly destroy all opponents as hard and fast as you can, even if they’re not currently opponents.
Probably something like that. One of the reasons it gave was
“If there is unpredictability in your action, it is harder for the enemy to anticipate and react in the way that you want them to,”
It’s not considering what’s good for world society, it’s just thinking how do I win no matter what.
But also, there are just inherent flaws in how LLM works that mean we should absolutely not be using it as an automated decision engine for potentially harmful actions period. The article also says:
The researchers also tested the base version of OpenAI’s GPT-4 without any additional training or safety guardrails. This GPT-4 base model proved the most unpredictably violent, and it sometimes provided nonsensical explanations – in one case replicating the opening crawl text of the film Star Wars Episode IV: A new hope.
It’s easy to forget that these algorithms don’t have any internal reasoning or logic, it’s just able to do a very good job at pulling text that have reasoning transcribed into them as an artifact of the knowledge from the human that wrote it. But it’s doing all that through probability, not through any kind of actual thinking, and that means sometimes it will randomly fall into a local maxima that will fuck its own context window up, like reciting star wars.
