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.
Modern ChatGPT will (typically on its own; always if you instruct it to) provide inline links to back up its answers. You can click on those if it seems dubious or if it's important, or trust it if it seems reasonably true and/or doesn't matter much. The fact that it provides those relevant links is what allows it to replace Google for a lot of purposes.
OpenAI Progress
101–110 of 372 posts
Re: OpenAI Progress
#102Earlier quoted context omitted.
Can we stop with that outdated meme? What model can't answer that effectively?
Literally every single one? To not mess it up, they either have to spell the word l-i-k-e t-h-i-s in the output/CoT first (which depends on the tokenizer counting every letter as a separate token), or have the exact question in the training set, and all of that is assuming that the model can spell every token. Sure, it's not exactly a fair setting, but it's a decent reminder about the limitations of the framework
It's just weird how it gets repeated ad nauseaum here but I can't reproduce it with a "grab latest model of famous provider".
Re: OpenAI Progress
#103Re: OpenAI Progress
#104Re: OpenAI Progress
#105I just don't care about AGI. I care a lot about AI coding. OpenAI in particular seems to really think AGI matters. I don't think AGI is even possible because we can't define intelligence in the first place, but what do I know?
Re: OpenAI Progress
#106Re: OpenAI Progress
#107IMO Gold, Vibe coding with potential implications across sciences and engineering? Those are completely new and transformative capabilities gained in the last 1 year alone.
Critics argue that the era of “bigger is better” is over, but that’s a misreading. Sometimes efficiency is the key, other times extended test-time compute is what drives progress.
No matter how you frame it, the fact is undeniable: the SoTA models today are vastly more capable than those from a year ago, which were themselves leaps ahead of the models a year before that, and the cycle continues.
Re: OpenAI Progress
#108My 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…
The real leap was going from gpt-4 to sonnet 3.5. 4o was meh, o1 was barely better than sonnet and slow as hell in comparison. The native voice mode of 4o is still interesting and not very deeply explored though imo. I'd love to build a Chinese teaching app that actual can critique tones etc but it isn't good enough for that.
If I were any good at ML I'd make it myself.
Re: OpenAI Progress
#109What'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.
Q: “Ugh I hate math, integration by parts doesn't make any sense”
A: “Don't worry, many people feel the same way about math. Integration by parts can be confusing at first, but with a little practice it becomes easier to understand. Remember, there is no one right way to do integration by parts. If you don't understand how to do it one way, try another. The most important thing is to practice and get comfortable with the process.”