I've shared this link before, showing Riley Goodside's artful prompt-crafting, but it's again relevant to this topic: https://twitter.com/goodside/status/1581805503897735168?s=20... GPT-3 is perfectly capable of recognizing what kinds of things it will be bad at, and can be encouraged to generate machine-executable queries to fill in that gap. (note this is based on prompting GPT-3, not chatGPT, but the principles about what this language model is capable of apply) It can even go one level deeper - there's an example there of it generating a python script that uses the 'wikipedia' library to look up the date of death of the Queen, as a way to fill in knowledge it doesn't have. Tell it it can use the wolframalpha module to answer questions that involve complex units, quantities, or advanced mathematics, and it'll almost certainly do that too. One of the things I love is this reply to that tweet - https://twitter.com/JulienMouchnino/status/15820120109127065... "How is it possible that GPT-3 understands what a human can compute in his/her head?" Riley's quick demo prompt that shows how well ChatGPT can guess whether particular mathematical results are easy to guess matches human intuition surprisingly well.
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View @goodside on Xyou can also tell the model that it doesnt know how to do math, and it respects that https://twitter.com/goodside/status/1568448128495534081
Also under that thread, a seemingly white background image that produces an ad when lasted on ChatGPT: https://x.com/goodside/status/1713000581587976372 This is a fantastic attack vector. It sounds like something from an Asimov novel.
I'd recommend following author of this thread for technical analysis of chatgpt and other models https://twitter.com/goodside/status/1598874674204618753?t=5W...