Researchers found that ChatGPT’s performance varied significantly over time, showing “wild fluctuations” in its ability to solve math problems, answer questions, generate code, and do visual reasoning between March and June 2022. In particular, ChatGPT’s accuracy in solving math problems dropped drastically from over 97% in March to just 2.4% in June for one test. ChatGPT also stopped explaining its reasoning for answers and responses over time, making it less transparent. While ChatGPT became “safer” by avoiding engaging with sensitive questions, researchers note that providing less rationale limits understanding of how the AI works. The study highlights the need to continuously monitor large language models to catch performance drifts over time.
I’ve found it making up “facts” when I query it. I thought I was doing something wrong, but apparently, it’s just changing the way it works for some reason.
Same. Now I’m only using search engines that don’t have it.
It’s not changing the way it works. It’s making up shit when it doesn’t know.
And that’s how AI works, it’s all probability. It’s not answering 2+2, there’s a probability that the answer is 4 and it chooses that. If something convinces it that it should be 5 it’ll start answering 5
That’s how language models work. It’s grouped into AI as is so many things, but it’s not AGI. It could open the doors to AGI as a component, but isn’t actually thinking about its answers. And those probabilities are driven by training reinforcement which includes the bias of giving an answer the human will receive well. Of course it’s going to “lie” or make up things if that improves the acceptance of the answer given.
The best description I’ve heard to give to most people is that llms knows what the right answer looks like, not what it is.
If I wanted that I could just ask my daughter. She makes up shit all the time when she doesn’t actually know.
Would probably be more fun that way too.
Perplexity.ai has been a solid addition to my internet searches.
Expect it’s Dog, not Snake. Bing thinks it’s Ox. How did the entire field of AI go from surprisingly accurate to utterly useless in the span of under a year? I have no idea what benefits you personally see in this site.
Was gonna say this too, it’s a great one for fact-checking. Sometimes it won’t include a source and make something up, just watch out for those.
How have you used Perplexity.ai?
Oh boy. I do research on it for various things. Florida released some laws changing alimony and I researched it via Perplexity to understand what the problem was. It worked. I understood the issue.
Or carbon capture technology.
In any case, I do look directly at the sources. Perplexity.ai is useful for framing a topic, getting the gist of it, but for being sure I know wtf is going on, I personally need to look at the sources.
Thanks for this reply. That’s probably the best way to use LLMs - general definitions or framing / summarizing of issues. And then always check the sources to make sure it was accurate. I’ve played around with ChatGPT and Bard and I think my mistake has been to be a little too granular or specific in my prompts. In most cases it produced results that were inaccurate (ETA: or flat out demonstrably wrong) or only fulfilled a part of the prompt.
I agree. The criticism that they’re not accurate kinda misses the point of LLMs being tools. It’d be like complaining that a hammer doesn’t jam the nail in all the way after the first stroke. Hit it again…and maybe try hitting it straight this time instead of at an angle. It’s an iterative process that can be self-correcting when done thoughtfully.