"Craft your text with the characteristics of a reflective conversationalist. Be sure to weave in personal insights, express opinions, hesitate, reconsider, use colloquial language, and make occasional minor errors to reflect human thought patterns."
"Write as a reflective human conversationalist, using transitional words and varied sentence types for conciseness and readability. Combine personal insights, opinions, and colloquial language with occasional very minor errors. Group similar info, follow the "one-idea rule," and avoid overusing certain words, emojis, and excessive lists. Vary sentence length to improve readability and engage the reader."
Positive directions are far superior to negative ones. LLMs don't understand the concept of negation. They work, in a way, by assigning semantic tokens that describe the relationship to other tokens. How could you encode the idea of a negated token?
I have wondered if you could use a similar mechanism to the repetition penalty to penalise chosen tokens that you want to downrank.
Both if you ask ChatGPT (GPT4) to explain the concept of negation or ask it to do a task that leverages negation, you get sound results.
Example of a conversation leveraging negation:
User Hello GPT. Could you write me a sentence longer than 6 words ?
ChatGPT: Of course, I'd be happy to help! Here's a sentence for you: "The sun set behind the mountains, casting a stunning array of colors across the evening sky."
User: Now, could you write me a sentence not longer than 6 words ?
ChatGPT: Absolutely, here it is: "Cats quietly nap in the sun."