That ChatGPT is good at truthfully answering one kind of prompt (analytic, where the facts are contained in the prompt, so the task is translation) and bad at t

That ChatGPT is good at truthfully answering one kind of prompt (analytic, where the facts are contained in the prompt, so the task is translation) and bad at truthfully answering another kind of prompt (synthetic, where the facts are not contained in the prompt, so the task is synthesis), tells me something about the nature of language. It turns out that setting up solutions to math problems is mainly a language translation task, followed by computation task as long as you have a way to eval() the resulting thunk: Do not perform calculations. Do not compute the answer. Use Javascript Math and Date to perform calculations. Wait for further questions. Show your work in the DESCRIPTION. The number of steps in the %%%THUNK%%% should match the number of steps in the %%%DESCRIPTION%%%. Always answer with Javascript compatible code in the %%%THUNK%%%, including numerical form without commas (eg, 238572348723). Always answer with this JSON compatible object form, eg, {"key":%%%VALUE%%%}: { "question": "4 days a week, Laura practices martial arts for 1.5 hours. Considering a week is 7 days, what is her average practice time per day each week?", "description":["Multiplying 1.5 and 4", "Dividing the step 1 answer by 7"], "thunk": "(function() { const step1 = 1.5 * 4; const answer = step1 / 7; return answer; })()" } Analytic augmentation is the procedure by which a question is augmented in a manner that promotes translation over synthesis. That this can be tested empirically is definitely something novel with regards to the philosophy of language!