I'm a bit confused here. Are you saying that if I zero out the system prompt on any LLM, including those fine-tuned to give answers in an instructional form, th

I'm a bit confused here. Are you saying that if I zero out the system prompt on any LLM, including those fine-tuned to give answers in an instructional form, they will follow your effect -- that nonsense prompts will get similar results to coherent prompts if they contain many of the same words? Because I've tried it on a few local models I have handy, and I don't see that happening at all. As someone else says, some of that difference is almost certainly due to supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) -- but it's weird to me, given the confidence you made your prediction, that you didn't exclude those from your original statement. I guess, maybe the real question here is: could you give me a more explicit example of how to show what you are trying to show? And explain why I'm not seeing it while running local models without system prompts?