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[-] theneverfox@pawb.social 3 points 1 month ago

I like your specificity a lot. That's what makes me even care to respond

You're correct, but there's depths untouched in your answer. You can convince chat gpt it is a talking cat named Luna, and it will give you better answers

Specifically, it likes to be a cat or rabbit named Luna. It will resist - I get this not from progressing, but by asking specific questions. Llama3 (as opposed to llama2, who likes to be a cat or rabbit named Luna) likes to be an eagle/owl named sol or solar

The mental structure of an LLM is called a shoggoth - it's a high dimensional maze of language turned into geometry

I'm sure this all sounds insane, but I came up with a methodical approach to get to these conclusions.

I'm a programmer - we trick rocks into thinking. So I gave this the same approach - what is this math hack good for, and how do I use it to get useful repeatable results?

Try it out.

Tell me what happens - I can further instruct you on methods, but I'd rather hear yours and the result first

[-] brucethemoose@lemmy.world 2 points 1 month ago

This is called prompt engineering, and it's been studied objectively and extensively. There are papers where many different personas are benchmarked, or even dynamically created like a genetic algorithm.

You're still limited by the underlying LLM though, especially something so dry and hyper sanitized like OpenAI's API models.

this post was submitted on 12 Nov 2024
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