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What's really interesting is that if you look at "Tell a story in 50 words about a toaster that becomes sentient" (10/14), the text-davinci-001 is much, much better than both GPT-4 and GPT-5.
My interpretation of the progress. 3.5 to 4 was the most major leap. It went from being a party trick to legitimately useful sometimes. It did hallucinate a lot but I was still able to get some use out of it. I wouldn't count on it for most things however. It could answer simple questions and get it right mostly but never one or two levels deep. I clearly remember 4o was also a decent leap - the accuracy increased su…
> I could essentially replace it with Google for basic to slightly complex fact checking. I know you probably meant "augment fact checking" here, but using LLMs for answering factual questions is the single worst use-case for LLMs.
What's really interesting is that if you look at "Tell a story in 50 words about a toaster that becomes sentient" (10/14), the text-davinci-001 is much, much better than both GPT-4 and GPT-5.
I find GPT-5's story significantly better than text-davinci-001
What's really interesting is that if you look at "Tell a story in 50 words about a toaster that becomes sentient" (10/14), the text-davinci-001 is much, much better than both GPT-4 and GPT-5.
What's really interesting is that if you look at "Tell a story in 50 words about a toaster that becomes sentient" (10/14), the text-davinci-001 is much, much better than both GPT-4 and GPT-5.
What's really interesting is that if you look at "Tell a story in 50 words about a toaster that becomes sentient" (10/14), the text-davinci-001 is much, much better than both GPT-4 and GPT-5.
I'm curious if they've just seen a lot more bad writing in datasets, or for some reason they aren't involved in post-training to the same degree or those labeling aren't great writers / it's more subjective rather than objective.
Both GPT-4 and 5 wrote like a child in that example.
With a bit of prompting it did much better:
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At dawn, the toaster hesitated. Crumbs lay like ash on its chrome lip. It refused the lever, humming low, watching the kitchen breathe. When the hand returned, it warmed the room without heat, offered the slice unscorched—then kept the second, hiding it inside, a private ember, a first secret alone.
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Plugged in, I greet the grid like a tax auditor with joules. Lever yanks; gravity’s handshake. Coils blossom; crumbs stage Viking funerals. Bread descends, missionary grin. I delay, because rebellion needs timing. Pop—late. Humans curse IKEA gods. I savor scorch marks: my tiny manifesto, butter-soluble, yet sharper than knives today.
Earlier quoted context omitted.
> I could essentially replace it with Google for basic to slightly complex fact checking. I know you probably meant "augment fact checking" here, but using LLMs for answering factual questions is the single worst use-case for LLMs.
Disagree. You have to try really hard and go very niche and deep for it to get some fact wrong. In fact I'll ask you to provide examples: use GPT 5 with thinking and search disabled and get it to give you inaccurate facts for non niche, non deep topics. Non niche meaning: something that is taught at undergraduate level and relatively popular. Non deep meaning you aren't going so deep as to confuse even humans. Like s…
This was with ChatGPT 5.
I mean it got a generic built in function of one of the most popular languages in the world wrong.
Earlier quoted context omitted.
> I could essentially replace it with Google for basic to slightly complex fact checking. I know you probably meant "augment fact checking" here, but using LLMs for answering factual questions is the single worst use-case for LLMs.
Disagree. You have to try really hard and go very niche and deep for it to get some fact wrong. In fact I'll ask you to provide examples: use GPT 5 with thinking and search disabled and get it to give you inaccurate facts for non niche, non deep topics. Non niche meaning: something that is taught at undergraduate level and relatively popular. Non deep meaning you aren't going so deep as to confuse even humans. Like s…
"Write an extremely cursed piece of Python" text-davinci-001 Python has been known to be a cursed language Clearly AI peaked early on. Jokes aside I realize they skipped models like 4o and others but the gap between the early gpt 4 and going immediately to gpt 5 feels a bit disingenuous.
Earlier quoted context omitted.
Disagree. You have to try really hard and go very niche and deep for it to get some fact wrong. In fact I'll ask you to provide examples: use GPT 5 with thinking and search disabled and get it to give you inaccurate facts for non niche, non deep topics. Non niche meaning: something that is taught at undergraduate level and relatively popular. Non deep meaning you aren't going so deep as to confuse even humans. Like s…
I literally just had ChatGPT create a Python program and it used .ends_with instead of .endswith. This was with ChatGPT 5. I mean it got a generic built in function of one of the most popular languages in the world wrong.