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.
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.