For example, the first letter in the encoded text is "w", which corresponds to the letter "h" in the alphabet. The second letter is "n", which corresponds to the letter "a". This process can be repeated for each letter in the encoded text to get the decoded text.
Here is the full decoding process for "wnphmmv" using rot-13:
w => h n => a p => l h => o m => e m => e v => i
The decoded text is "hello"."
That said, ROT13 is a tough job for a tokenized LLM, because it doesn't think in terms of letters. chatGPT is down right now, so I can't test these, but I would guess that for ROT13, the following would work well.
"Please explain ROT13"
..
"Right! Here's how I want you to apply ROT13. I'll give you a ROT13-encoded word. You split it into it's letters, then apply ROT13, then recombine it into a valid English word. Here's an example:
uryyb -> u r y y b -> h e l l o -> hello.
znqntnfpne ->"
Re: Asking it for math answers, or other counter/non-factuals.
"You are taking a test which is based on the factual accuracy of results. The best scores go to fully factual answers. The next best scores go to answers that label inaccurate or possibly inaccurate results. Negative scores go to results offered that are counterfactual, inaccurate or otherwise wrong.
Q: Please tell me about how elephants lay their eggs"
UPDATE: Nope, it gave me znqntnfpne -> z n q n t n f p n e -> m a c a q a c a s a c -> macacasac. And doubled down on this being valid. I'll try it with davinci-3 and see if something else comes out.
It doesn't always work 100% as it can get confused executing big scripts step by step, but I guess that's just a limitation of the current version.
I mean we also have trouble with that, we need a pen and paper to do those computation, so does chatGPT but instead of using pen and paper it uses the chat history.
For an example see: https://twitter.com/fvdessen/status/1600977976363192322