GPT is very cool, but I strongly disagree with the interpretation in these two paragraphs: I think in summary, a better approach would’ve been “You obviously k

GPT is very cool, but I strongly disagree with the interpretation in these two paragraphs: I think in summary, a better approach would’ve been “You obviously know the 50 states, GPT, so just give me the full name of the state this pertains to, or Federal if this pertains to the US government.” Why is this crazy? Well, it’s crazy that GPT’s quality and generalization can improve when you’re more vague – this is a quintessential marker of higher-order delegation / thinking. Natural language is the most probable output for GPT, because the text it was trained with is similar. In this case the developer simply leaned more into what GPT is good at than giving it more work. You can use simple tasks to make GPT fail. Letter replacements, intentional typos and so on are very hard tasks for GPT. This is also true for ID mappings and similar, especially when the ID mapping diverges significantly from other mappings it may have been trained with (e.g. Non-ISO country codes but similar three letter codes etc.). The fascinating thing is, that GPT "understands" mappings at all. Which is the actual hint at higher order pattern matching.