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
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.
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
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.