A tool that gives at least 40% wrong answers, used to find 90% errors?
AI finds errors in 90% of Wikipedia's best articles
Submitted 3 weeks ago by King@blackneon.net to technology@lemmy.world
https://en.wikipedia.org/wiki/Wikipedia:Wikipedia_Signpost/2025-12-01/Opinion
Comments
Stefan_S_from_H@discuss.tchncs.de 3 weeks ago
AcesFullOfKings@feddit.uk 3 weeks ago
[deleted]echodot@feddit.uk 2 weeks ago
But we don’t know what the false positive rate is either? How many submissions were blocked that shouldn’t have been, it seems like you don’t have a way to even find that metric out unless somebody complained about it.
s@piefed.world 3 weeks ago
acosmichippo@lemmy.world 3 weeks ago
90% errors isn’t accurate. It’s not that 90% of all facts in wikipedia are wrong. 90% of the featured articles contained at least one error, so the articles were still mostly correct.
pulsewidth@lemmy.world 2 weeks ago
And the featured articles are usually quite large. As an example, today’s featured article is on a type of crab - the article is over 3,700 words with 129 references and 30-something books in the bibliography.
Ita not particularly unreasonable or unsurprising to be able to find a single error amongst articles that complex.
amateurcrastinator@lemmy.world 3 weeks ago
Bias needs to be reinforced!
helpImTrappedOnline@lemmy.world 3 weeks ago
The first edit was a undoing a vandalism that persisted for 5 years. Someone changed the number of floors a building had from 67, to 70.
A friendly reminder to only use Wikipedia as a summary/reference aggregate for serious research.
This is a cool tool for checking these sorts of things, run everything through the LLM to flag errors and go after them like a wack-a-mole game instead of a hidden object game.
mika_mika@lemmy.world 2 weeks ago
Hehe 67
crypt0cler1c@infosec.pub 3 weeks ago
This is way overblown. Wikipedia is on par with the most accurate Encyclopedias with 3-4 factual errors per article.
TheBlackLounge@lemmy.zip 3 weeks ago
More like 1, sometimes 2, errors in 90% of wikipedia’s longest and most active articles.
chronicledmonocle@lemmy.world 2 weeks ago
Congrats. You just burned down 4 trees in the rainforest for every article you had an LLM analyze.
LLMs can be incredibly useful, but everybody forgets how much of an environmental nightmare this shit is.
GooseFinger@sh.itjust.works 2 weeks ago
Had to look up Chat GPT’s energy usage because you made me curious.
Seems like Open AI claims Chat GPT 4o uses about 0.34 Wh per “query.” This is apparently consistent with third party estimates. The average Google search is about 0.03 Wh, for reference.
Issue is, “query” isn’t defined, and it’s possible this figure is the energy consumption of the GPUs alone, omitting additional sources that comprise the full picture (energy conversion loss, cooling, infrastructure, etc.). It’s also unclear if this figure was obtained during model training, or during normal use.
I also briefly saw that Chat GPT 5 uses between 18-40 Wh per query, so 100x more than GPT 4o. The OP used GPT 5.
It sounds like the energy consumption is relatively bad no matter how it’s spun, but consider that it replaces other forms of compute and reduces workload for people, and the net energy tradeoff may not be that bad. Consider the task from the OP - how much longer/how many more people would it take to accomplish the same result that GPT 5 and the lone author accomplished? I bet the net energy difference isn’t that far from zero.
Here’s the article I found: towardsdatascience.com/lets-analyze-openais-claim…
REDACTED@infosec.pub 2 weeks ago
How would this compare to one person with 5090 gaming for a week?
Kellenved@sh.itjust.works 2 weeks ago
This is my number 1 reason to oppose AI. It is not worth the damage.
Gonzako@lemmy.world 2 weeks ago
“Liar thinks truth is also a lie. More at 11”
kepix@lemmy.world 3 weeks ago
the tool that is mainly based on wikipedia info?
x00z@lemmy.world 3 weeks ago
The tool doesn’t just check the text for errors it would know of. It can also check sources, compare articles, and find inconsistencies within the article itself.
There’s a list of the problems it found that often explains where it got the correct information from.
echodot@feddit.uk 2 weeks ago
The problem is a lot of this is almost impossible to actually verify. After all if an article says a skyscraper has 70 stories even people working in the building may not be able to necessarily verify that.
I have worked in a building where the elevator only went to every other floor, and I must have been in that building for at least 3 months before I noticed because the ground floor obviously had access and the floor I worked on just happened to do have an elevator so it never occurred to me that there may be other floors not listed.
For something the size of a 63 (or whatever it actually was) story building it’s not really visually apparent from the outside either, you’d really have to put in the effort to count the windows. Plus often times the facade looks like more stories so even counting the windows doesn’t necessarily give you an accurate answer not that anyone would necessarily have the inclination to do so. So yeah, I’m not surprised that errors like that exist.
More to the point the bigger issue is can the AI actually prove that it is correct. In the article there was contradictory information in official sources so how does the AI know which one was the right one? Could somebody be employed to go check? Presumably even the building management don’t know the article is incorrect otherwise they would have been inclined to fix it.
kalkulat@lemmy.world 3 weeks ago
Finding inconsistencies is not so hard. Pointing them out might be a -little- useful. But resolving them based on trustworthy sources can be a -lot- harder. Most science papers require privileged access. Many news stories may have been grounded in old, mistaken histories … if not on outright guesses, distortions or even lies. (The older the history, the worse.)
And, since LLMs are usually incapable of citing sources for their own (often batshit) claims any – where will ‘the right answers’ come from? I’ve seen LLMs, when questioned again, apologize that their previous answers were wrong.
architect@thelemmy.club 2 weeks ago
Which LLMs are incapable of citing sources?
jacksilver@lemmy.world 2 weeks ago
All of them. If you’re seeing sources cited, it means it’s a RAG (LLM with extra bits). The extra bits make a big difference as it means the response is limited to a select few points of reference and isn’t comparing all known knowledge on a subject matter.
kalkulat@lemmy.world 2 weeks ago
To quote ChatGPT:
“Large Language Models (LLMs) like ChatGPT cannot accurately cite sources because they do not have access to the internet and often generate fabricated references. This limitation is common across many LLMs, making them unreliable for tasks that require precise source citation.”
W3dd1e@lemmy.zip 2 weeks ago
This headline is a bit misleading. The article also says that only 2/3 of the errors GPT found were verified errors (according to the author).
- Overall, ChatGPT identified 56 supposed errors in these 31 featured articles.
GeneralEmergency@lemmy.world 3 weeks ago
No surprise.
Wikipedia ain’t the bastion of facts that lemmites make them out to be.
It’s a mess of personal fiefdoms run by people with way too much time on their hands and an ego to match.
naeap@sopuli.xyz 3 weeks ago
Yeah, better to use grokpedia /s
GeneralEmergency@lemmy.world 2 weeks ago
I know this is sarcasm, but in case people don’t know.
Oh Jesus Christ no. At least Wikipedia has some form of oversight from multiple sources and people.
pulsewidth@lemmy.world 2 weeks ago
Disagree, Wikipedia is a pretty reliable bastion of facts due to its editorial demands for citations and rigorous style guides etc.
Can you point out any of these personal fiefdoms so we can see what you’re referring to?
selokichtli@lemmy.ml 2 weeks ago
Just wanted to point out there insane disparity between these cost of running Wikipedia and that of ChatGPT. The question here is not if LLMs are useful for sine things, rather than if it’s worth it for most things.
Tollana1234567@lemmy.today 2 weeks ago
wikipedia does have some outdated info on certain things, mostly with certain species/discovery phylogeny.
dukemirage@lemmy.world 3 weeks ago
legitimate use of a LLM
anamethatisnt@sopuli.xyz 3 weeks ago
I find that an extremely simplified way of finding out whether the use of an LLM is good or not is whether the output from it is used as a finished product or not. Here the human uses it to identify possible errors and then verify the LLM output before acting and the use of AI isn’t mentioned at all for the corrections.
The only danger I see is that errors the LLM didn’t find will continue to go undiscovered, but they probably would be undiscovered without the use of the LLM too.
porcoesphino@mander.xyz 3 weeks ago
I think the first part you wrote is a but hard to parse but I think this is related.
I think the problematic part of most genAI use cases is validation at the end. If you’re doing something that has a large amount of exploration but a small amount of validation, like this, then it’s useful.
A friend was using it to learn the linux command line, that can be framed as having a single command at the end that you copy, paste and validate. That isn’t perfect because the explanation could still be off and it wouldn’t be validated but I think it’s still a better use case than most.
shiroininja@lemmy.world 3 weeks ago
Or it flags something as an error falsely and the human has so much faith in the system that it must be correct, and either wastes time finding the solution or bends reality to “correct” it in a human form of hallucinating bs
ordnance_qf_17_pounder@reddthat.com 3 weeks ago
“AI” summed up. 95% of the time it’s pointless bullshit being shoehorned into absolutely everything. 5% of the time it can be useful.
dukemirage@lemmy.world 3 weeks ago
like Comic Sans
Treczoks@lemmy.world 3 weeks ago
Yep. Let it flag potential problems, and have humans react to it, e.g. by reviewing and correcting things manually. AI can do a lot of things quick and efficiently, but it must be supervised like a toddler.
buffing_lecturer@leminal.space 3 weeks ago
This is an interesting idea:
architect@thelemmy.club 2 weeks ago
So… the same as most employees but cheaper.
People here are above average and overestimate the vast majority of humanity.
passepartout@feddit.org 3 weeks ago
Yes and no. I have enjoyed reading through this approach, but it seems like a slippery slope from this to “vibe knowledge” where LLMs are used for actually trying to add / infer information.
LastYearsIrritant@sopuli.xyz 3 weeks ago
Don’t discard a good technique cause it can be implemented poorly.
architect@thelemmy.club 2 weeks ago
The issue is that some people are lazy cheaters no matter what you do. Banning every tool because of those people isn’t helpful to the rest of humanity.
de_lancre@lemmy.world 3 weeks ago
Wait, you mean using Large Language Model that created to parse walls of text, to parse walls of text, is a legit use?
Those kids at openai would’ve been very upset if they could read.
lightnsfw@reddthat.com 2 weeks ago
Even for that it’s mid at best. I try using co-pilot at work often and it makes shit up constantly.
dukemirage@lemmy.world 2 weeks ago
Chatbots aren’t the worst use case, too, even though we are headed in a wrong direction.