Yes. I have been iterating on the prompt for a while. Here are a few techniques that make it sound more like me.
I tell it to describe “lsusr”. In particular, what makes me different from other writers similar to me. Then I tell it to emphasize those things. I also say “lsusr” many times and use it as an adjective. I don’t know if this works but my intuition says it is natural for an LLM to understand.
I have it write a draft, then I tell it to tell me how it missed the mark, and to fix those mistakes. This prevents overfitting on my words. If I tell it to be “bold”, for example, it will overfit on “bold” instead of copying me along many dimensions. More generally, I don’t describe myself to ChatGPT. That results in ChatGPT copying my description of me instead of actual me. I let ChatGPT describe me, and then tell ChatGPT to write like it just described, but more so.
Often something ChatGPT writes will use a word like “Bayesian” that is associated with writers like me but which I don’t use much. Telling ChatGPT not to use specific words seems to improve its output without causing distortive side-effects.
Next step would be to try it on Claude, and on o1-mini/preview (the iterative revising should work for both, like it did with my Rubik’s Cube exercise). If you are in the base model adequately, then you should be in Llama-3-405b-base as well, and that’s available through a few APIs now, I believe, and you may find it to work a lot better if you can get the prompt right—several of your complaints like unsettlingness, groupthink, jokes, or indirection are characteristic of mode-collapsed tuned models but not base models.
Yes. I have been iterating on the prompt for a while. Here are a few techniques that make it sound more like me.
I tell it to describe “lsusr”. In particular, what makes me different from other writers similar to me. Then I tell it to emphasize those things. I also say “lsusr” many times and use it as an adjective. I don’t know if this works but my intuition says it is natural for an LLM to understand.
I have it write a draft, then I tell it to tell me how it missed the mark, and to fix those mistakes. This prevents overfitting on my words. If I tell it to be “bold”, for example, it will overfit on “bold” instead of copying me along many dimensions. More generally, I don’t describe myself to ChatGPT. That results in ChatGPT copying my description of me instead of actual me. I let ChatGPT describe me, and then tell ChatGPT to write like it just described, but more so.
Often something ChatGPT writes will use a word like “Bayesian” that is associated with writers like me but which I don’t use much. Telling ChatGPT not to use specific words seems to improve its output without causing distortive side-effects.
Next step would be to try it on Claude, and on o1-mini/preview (the iterative revising should work for both, like it did with my Rubik’s Cube exercise). If you are in the base model adequately, then you should be in Llama-3-405b-base as well, and that’s available through a few APIs now, I believe, and you may find it to work a lot better if you can get the prompt right—several of your complaints like unsettlingness, groupthink, jokes, or indirection are characteristic of mode-collapsed tuned models but not base models.