You can prompt it to fact check. Getting tired of these slippery slope arguments that never pan out in the hopeless way people think (computers will take all the jobs etc)... but I don't think it will ever stop, it seems inherent to human behavior or something.
The fact that AI can be duped more easily than a human while sounding more authoritative and being entrusted with more and more critical tasks is really not something you should feel compelled to defend or relativize. It's a problem and the industry doesn't seem to have a good fix.
> the feared bottom of the slope was never reached
Hard disagree. Sometimes it's avoided, any other time we just go into cope mode. We discover on the ride down that the bottom isn't so bad. Turns out our collective control mechanisms didn't fail ourselves if the initial assessment was wrong.
Often real people don't either, plenty of fraud or selective reporting out there... but with AI we can also launder it so that the people who do care about fake data don't know any better.
I wonder if Googles products will eventually be the least vulnerable to this after spending so many years optimizing how they filter quality content from bad content.
Considering Google’s product was the one who told people to eat rocks, put glue on pizza, and their search results have been dominated by SEO spam for years, I’m not optimistic.
Reminds me of the people whining about Wikipedia. AI can know or care if the data is fake, it just needs to be programmed to cross check or rate the source. You can even tell it to do that before accepting the data.
Is this categorization actionable or meaningful in any way? If a system or process relies on no one on the internet being a liar, it's broken. There are liars. There will be more liars. As is made evident, people (and machines) will unwittingly reproduce lies out of sheer incompetence. That reflects poorly on them as reliable sources of information.
I'm reminded of a section of Cory Doctorow's latest non-fiction, The Reverse Centaur's Guide To Life After AI (it is of course an excellent read), where he discusses how the semantic web and having an internet set-up for agents and AI to use won't work:
>It also hasn't happened because there is a reward for cheating. Scammers will label their phone numbers as the customer service number for your cable operator or bank. Counterfeiters will label their batteries or phone cases as official accessories. It's hard enough to get people to correctly label their stuff—but fact-checking billions of labels created by millions of people, none of whom you know personally or whose identities you can verify? That's insurmountable.
There's more he says—the preceding paragraph is below—but that line "there is a reward for cheating" is something I keep thinking about. It's hard not to see in so many of the discussions of LLMs.
>For one thing, there are irreconcilable differences in the ways in which things should be described. A site run by transphobes isn't going to label every story about a trans woman as "a story about a woman," irrespective of what the standard says. Microsoft is never going to label Excel as equivalent to Apple's Numbers, Google Sheets, and LibreOffice's Calc. Hermès is never going to list a $40,000 handbag as being made of the same material and having the same capacity as a $30 leather handbag of the same size and shape.
IMO, it's similar to what's happening with code. People have been shipping sloppy code and fake news forever. It's just that the cost of doing these things has fallen off a cliff.
Of course. But when you see text in the descriptor of a satchel on a satchel seller's site, you contextualize it as marketing. If you saw the same text in a textbook on the history, design, and manufacturing of satchels, you would contextualize it very differently.
When an LLM is the lens through which you view that text, you often don't have the context of where it came from, and the LLM is often really bad at filtering trustworthiness based on context when retrieving information to answer a query with.
I wouldn't go to a random site with a sketchy URL to get the customer service number for my xyz. I'd go to their official domain. Thus successfully navigating the treacherous waters scammers attempt to muddy.
Yea if you ever want to test how gullible even latest SOTA models are ask it for financial advice on a specific IPO and watch it vaccume up sell side nonsense and spew it out back to you as absolute fact.
It has little to do with gullibility and more to do with the way they do things.
In principle AI Agents are superhuman at codeforces but tons of these vibe coded apps have ridiculously stupid performance problems.
The problem is a utilization of expertise thing, it can tell you with incredible detail about pros and cons and caveats to a problem if you ask about it. Truly superhumanly intelligent.
Calibration when it comes to actual decision making takes an incredible reduction in capability after that. Maybe it has something to do with the sycophancy or something but its like someone with exactly zero opinions (and therefore, taste).
My experience is that you can brainstorm with it on "Do A or B?" and it will produce a coinflip answer A, then if you continue and ask about a specific concern "what about this aspect of B?", it immediately 180s and flips back to B being way better than A.
Acting on this expertise when decision making points arise naturally for itself is where everything really nosedives. It's just gonna do whatever.
So when you are doing this "financial advice please" questioning, it is entirely hitting these weaknesses squarely in the face.
You’re talking to the guy that invented next word and phrase token prediction at Google.
Gullible is an appropriate colloquialism here. As in they don’t check countervailing narratives naturally and fall into a recency sink on last context. Long walk context construction is hard, time consuming and most
Importantly significantly more expensive.
There is nothing stopping us from adding the ability to understand the context of information an LLM consumes, too, right? The same process humans use to assess data reliability could be taught to an LLM?
If real people are using the model for real, they will discover for themselves that the data isn’t reliable, and then might switch to a different model.
The incentive will be to maintain a subscriber base.
We have laws against undisclosed and misleading advertising, because consumers can't make informed purchases otherwise. What makes you think LLMs will have a fundamentally different pattern than everything else?
And the level of effort required to have these laws applied to false marketers is unrealistic. Unless there is a case of disfigurement or death, it's just caveat emptor.
> If real people are using the model for real, they will discover for themselves that the data isn’t reliable, and then might switch to a different model.
Insanely optimistic view to take. I know a handful of people who make this effort in my personal life, including me, and we tend to be treated as paranoid for caring.
Maybe. With enough money and resources and time, maybe. But it would need to be very reliable to be at all useful, and that level of reliability is very hard to achieve with something so nuanced and complex.
You have an entire lifetime of experience and the wisdom of countless generations to inform you of the context with which you should process new information. You were specifically trained for years to be able to do this somewhat effectively. Doing the same for an LLM may or may not be possible. They contain much of that experience in some form, but are not well equipped to make use of it iteratively. It is incredibly expensive to attempt even a simple and ineffective version of it with our current methods.
If the LLM can achieve even 90% accuracy, it becomes more of a hindrance than a help. Do you make use of the function and trust it successfully understood the provenance of new information? If so, ten percent of the time you will be misled, which could be disastrous across a population. If you don't simply trust it, then do you painstakingly check the provenance of everything it tells you? Of course not, you already don't do that with current search engines and blogs and such. Yet falling somewhere in between would be so much worse than simply not having the LLM attempt to discern trustworthiness from providence in the first place.
But all of that is opinion and I may be incorrect. Perhaps someone will create some ingenious way of imparting wisdom and discernment to LLMs next year and this will all be trivial.
I would suggest that llms could access information more reliably and securely than humans ever could.
The padlock on browsers means nothing to most humans. Permission popups are just there to be immediately accepted so that one can start scrolling reels sooner rather than later.
I agree and finding "official domains" is an interesting problem in and of itself. I wonder if PageRank like weights of input data would help, or some web of trust-like idea to prune blatant spam websites.
The primary purpose of Google is to stop that. You have 100% trust in Google results because of the effort they have put into cleaning the results you see.
Billions have been spent on it.
Try searching Bing sometime. First result for Firefox is a malware clone site (you’ll have to scroll past the malware ad to see it).
This is completely and utterly wrong. Google may put some effort into catching malware - but even that is limited to the more obvious cases. But for almost everything else, they put between 0 effort and negative effort into trustworthiness of the sources (e.g. they clearly favor big businesses for certain product searches regardless of quality).
> Isn't all of this true for a human-used internet as well?
Yes, but if you read this "semantic" info directly from a site, you can judge the site and its potential biases, and decide for yourself "this sells Louis Vuitton, of course they will praise their handbags". Or to pick a more extreme example that nobody would ignore, if you somehow land on an extremist website spewing racist crap, your alarms will surely trigger by anything they say.
But through an LLM this gets whitewashed and presented as authoritative and "neutral", when it isn't. You might know not to trust the LLM either (if you're paying attention; we all have our unconscious biases) but I bet you most people will be caught off guard.
How do you get the poison out of the water in the well? I understand the way this is supposed to work on the web, there are support channels to deal with this sort of thing. But if the deceit is baked into the model, what is your recourse?
Same as always: Develop a healthy sense of skepticism, cultivate a stable and diverse network of sources whose credibility you understand, don’t trust what you read, and certainly don’t trust synthesis unless you know how it’s made?
So “caveat emptor”? I get that’s always part of reality but we shouldn’t exactly steer into that skid IMO. It just shifts the burden to the rest of us and does nothing to address the root cause
It is, and always has been, the only possibility. It's up to every individual to check for themselves what is true and what is not, and to decide who to trust for things that they can't check.
The real lesson is this: trust in the source is the most important thing for 99.99% of what you read. You will sometimes read about facts that you can actually verify, but those are mostly useless in the grand scheme - except as a sanity check on the source. For everything else, you have to ultimately trust the source.
Now sure, sometimes you can get lucky and detect certain internal inconsistencies that prove that something can't be true. And to some extent, reading the same information from multiple sources you don't normally trust too much can be a slight signal that it might be true. But these are generally in the minority.
So you test every sip of water you drink? Inspect every road before you drive down it? Check every bathroom product for harmful additives?
Again I am not ignoring reality. But for society to work and for us to live our daily lives with any semblance of sanity, we can't be expected to be fully informed on everything we see, touch, consume, etc. We have to be able to make reasonable assumptions about safety and accuracy or we can't function. And the time it takes to vet stuff is often a privilege (or you can brute force pay your way to safety).
I do not rigorously audit everything I read on wikipedia. I assume a certain baseline of accuracy up to a point, with of course exceptions. Do you pull up 3-4 high quality sources for everything you read or claim? Are you that academic about it?
I think the point is that for those you are trusting the regulators. For stuff you read online you are either trusting the source of the information or not. You should choose what information sources you trust carefully.
My whole point is that you have to trust people, that this is the only thing you can rely on for almost every important thing you care about. For food, for example, I don't eat from sketchy restaurants, unless I get some personal recommendations from friends about it. That is, I only eat from places where I think I have some reason to trust that the food is sanitary.
Also, I didn't say you need to access high quality sources for everything, I again kind of said the opposite: you choose who to trust for what, and that's that. There is nothing more, and has never been anything more, that you can do. Sometimes people and sources you trust will still lie to you, or be wrong, and you'll know wrong information - this is also unavoidable.
Of course, the more important some information is to you, the more effort you should actually spend trying to verify with multiple trusted sources. Some weird fact about cats you saw in a TikTok? Fine to just believe it. Trying to figure out who to vote for, or if the elections were stolen? Probably worth checking a few more sources. Read some definition for a word on Wikipedia, or some old historical event? Probably fine. Reading about an even slightly controversial topic? Check the actual sources, don't trust the snapshot you happen to see*.
Of course, the way LLMs work is antithetical to this, as information provided directly by the LLM is not coming from any human source you can decide if you should trust or not. So you shouldn't trust it for anything important, unless you can verify. But for information or disinformation on the web, nothing has changed, except maybe the quantities.
*for a silly example: the exact definition of the word "electrocution" is a slightly controversial topic - whether electrocution refers to any (significant) injury caused by electricity, or only death. Pedants soemwaht regularly edit the article on Wikipedia to remove the "or serious injury" part of the definition, despite it carrying a link to the OED that includes this mention.
Once this provenance trail, that humans use to trace claims back to an accountable source, is dead and buried under automated sludge, "just pick people to trust" stops working as a viable strategy. How do you even tell who is actually talking?
Seriously we’ve got Reddit now straight up translating foreign language comments with AI and without stating they did it. That is a massive decision to make, and I feel like no one cares.
I don't think it's necessarily so. I view it more like garbage collection algorithms - the amount of garbage doesn't actually matter, you should only be looking at the roots of trust and down from there, and those will generally only lead you to more trustworthy content. The WSJ won't randomly link to fake TikTok accounts.
Now, the bigger problem happens if some of the trusted institutions and people go bad, for various reasons. If the NYT got bought out by Alex Jones, it might start mixing in lots of slop - but this is also a pre-existing problem.
it seems like end stage of LLMs is an MCP economy in which Google and Apple will have a massive advantage in verified locations, businesses, identities and representation of services accurately.
How can this be fixed? Nothing prevents you from publishing straight BS, and LLMs will use any site that doesn't have a garbage reputation as a valid third-party source.
Of course, LLMs already use Reddit, Facebook and co. as a source, so it doesn't really matter...
> How can this be fixed? Nothing prevents you from publishing straight BS, and LLMs will use any site that doesn't have a garbage reputation as a valid third-party source.
Personally, I hope it can't, or the fix is onerous and kneecaps a lot of the utility of AIs.
I wonder if we can even have some adversarial competition, where competing businesses try to get AIs to ingest bad data to pollute their competitors decision-making.
For starters: lets get some fake math papers out there. Given proof-checking systems, maybe they won't totally stop OpenAI, but hopefully the bad math could at least drive up the cost and slow AI down.
Ah, yes, arrogance of humans is unbounded. That you, humble reader are happier to swim in a sea of misinformation on the premise that you are smarter and faster at disambiguating bullshit from reality.
Furthermore, you are content to pretend that the rest of humanity is capable of doing this on a regular basis. And not just the top 10% of those who can read, interpret and weigh biases, but a solid portion of it.
It is a completely reasonable interpretation if you are an accelerationist living in the elite classes. In fact, perhaps, they are even counting on a majority of people thinking this is a fine environment.
I don't have the answers, but a lot of this is just a continuation of a very bad trend.
Dude. The internet has been poisoned for a very long time. Remember Viagra? It used to be easy to call out the BS. But if you are outsourcing your thinking to a LLM, it’s much harder.
The fix that seems obvious to me is to only let the LLM see truthful (or at least non-malicious) data. The model trainers carefully curate the training data, and you don't give the LLM access to full-web search. Instead, you hook them up to an 'official' source like an encyclopedia, documentation for the software in question, etc.
It's a ton of effort and expense, but probably feasible from a technical point of view. So instead, the model providers will probably do something silly like wire up rules like "NYT=Reliable;my.blog.forbes.com=Unreliable" and they'll say the issue is solved.
It cannot be fixed. The most hilarious example of it is YouTube. They have a video summarisation feature, which automatically creates video summaries that then get fed into the search engine. The problem with those is that they are created using models that are good few months behind, and let's say someone published a video on Nikon ZR and the model used to create summaries does not know about Nikon ZR and inserts references to Nikon Z8 or Nikon Zf into the summaries. This decreases accuracy of YouTube search and negatively affect discoverability. It's the AI version of shitting into the well you drink from. Bon appetit!
Defer to authority and trust your government to rank what is truth and what is not, so your LLM always gives you the right answer. Nothing can go wrong here ever.
People stop trusting what they read on the internet.
All alternates are just attempting to improve the baseline trustworthiness of random information from the internet (and/or LLM that's baby-birding it back to you). Any increase in how much people blindly trust random stuff from the internet only serves to increase the blast radius when it is wrong.
My personal belief is that AI is eventually going to force the populous back to more face-to-face interactions. There will be a breaking point where people can’t take the “is this a human or a bot” guessing game any longer.
This is my hope, I'm not sure I believe it will happen, at least not in my life time. Despite reading this tech forum right now, I've been taking steps to heavily reduce my reliance on technology-for-technology's sake. But how you encourage others to? I'm not totally sure. So far I talk about it (to people, face-to-face) but everyone is still staring at their smartphone.
I have a strong suspicion that this will be the true butlerian jihad of our world. We have already started seeing information silos in the form of discord groups. Social media users are increasingly privatising their pages, allowing access for known users only. Reddit is still surviving because of the large userbase there.
I don't think it is up to us at this point. LLMs are now the lens ("filter for what people see"). I hope, they will fix this. I think that they will still rely on reasoning and better grounding, which should prevent straight lies. But I think two bigger problems here: 1. training data might have the BS which would steer reasoning; 2. grounding might still be limited by small number of sources.
You can find people complaining that Google search results decayed to being unusable prior to LLMs being a big thing. The marketers and spammers effectively won that fight. They'll presumably win this one too.
1. This is a 2022 era picture that LLMs just suck up what's on the Internet and regurgitate it. It hasn't been true for a long time.
2. There's almost no one who has a stronger incentive to distinguish LLM generated text from non-generated LLM text. Training classifiers is pretty manageable.
>1. This is a 2022 era picture that LLMs just suck up what's on the Internet and regurgitate it. It hasn't been true for a long time
if anything it’s more true now, AI chatbots rarely use raw LLM output, instead they have CoT and use search tool calls. and the speed at which webpages are crawled is in the matter of hours. I posted something here on hn, then searched for it on google 1 hr later and its AI answered with my comment
there’s several companies right now making a killing of organic placement on chatbots similar to the SEO rush of early google
> 2) There's almost no one who has a stronger incentive to distinguish LLM generated text from non-generated LLM text. Training classifiers is pretty manageable
i think it’s pretty naive to think that if LLM companies would develop LLM detection they would use it to skip training on that content.
instead, they use it to train their output to be less detectable.
LLM detection is not solvable because it is adversarial, an arms race
>1. This is a 2022 era picture that LLMs just suck up what's on the Internet and regurgitate it. It hasn't been true for a long time.
When I ask ChatGPT certain questions, it very clearly shows me the sources it's searching through in the form of favicons, and they're always the favicons of sites that are in the first 5-10 results for a Google search query that would fit what I've asked the LLM. It will then cite those websites as it's source, with a direct link to the page that also shows up in an equivalent Google search. It does that daily.
Why do you believe that? The favicons seem like they’re generated by the UI, not the LLM.
On the other hand, I recently asked ChatGPT for some information about a group of people, and the headshots it put with each summary were very clearly not the people it was describing. So hallucinations do still occur even on the latest models, it just seems unlikely that the favicons would be LLM-generated.
The other day I asked Gemini to evaluate if a Reddit post was true. It told me it was definitely true because it had been verified by legitimate news agencies that had reported on the incident. These "legitimate" news agencies were an AI aggregator that just had a direct summary of the post with a disclaimer that it wasn't independently verified and a yahoo news "article" that was just the same AI summary. The AI summary included hallucinations that weren't even part of the original reddit post (like giving the OP a gender when no genders were mentioned).
The top 10 results were getting pretty bad even pre-LLM. I remember tons of poorly written articles near the top, just gaming the system with SEO. Everyone was paying some guy on Fiverr $5 an article to churn out hundreds of articles with little regard for accuracy.
Crucially, by visiting those websites you gathered a bunch of context clues which told you “this source is trash; I shouldn’t trust it and should look somewhere else”. When you ask an LLM, every source is treated as equally valid. You may have gotten an answer from the utmost professional on the subject or an obvious (to you) scam, they are presented the same.
Even if you are in the minority who asks for sources and truly verifies them, they are often made up (don’t exist) or don’t say what the LLM said they said.
Pre Google internet problem was not about the quality but about spam in headers and keywords, which were used by the pre-google search engines.
Google came and worked wonderfully for a year or two, until people learned to game page rank algorithm. Soon first google pages were just SEO spam and now half of the google is just spam, big corp websites, etc.
All of that precious stuff from small hobby websites about cool things to blogs that used to be wonderful read like https://www.joelonsoftware.com.
AI were not hurt that much by spam, I guess there were at least some curation done, the question is if this curation will work and for how long. It seems we are throwing the towel here as in case of search.
Yea, I started out thinking "well you just use the thing to find citations." Then I realized all this article is just: "I lied on the internet and people believed me, isn't that crazy!?!"
This type of behavior is why gatekeeping institutions exist. We all pay a non-trivial cost because someone thinks that other people taking your lies seriously is somehow remarkable. It's not.
Research is hard work. Publishing fake research is just intentionally throwing wrenching into the machine of academia.
This problem is so bad that you can create a website just to review your own website so when LLMs want to pull out reviews for the website the fake reviews count
> In cartography, a trap street is a fictitious entry in the form of a misrepresented street on a map, often outside the area the map nominally covers, for the purpose of "trapping" plagiarists of the map who, if caught, would be unable to explain the inclusion of the "trap street" on their map as innocent.
This is fascinating . I wonder what the mechanism was that punished someone who copied the map. Copyright laws? This appears to be an old strategy so im not sure thats the case.
Other than some sort of social punishment I don’t see the cost for the copier. For the original author there is a cost of making your map less accurate on purpose.
They're used by map makers to prevent other map makers from freeriding on the original work. They're less worried about personal copies and much more interested in deterring commercial reuse.
That doesn't answer my questions. What is the mechanism punishing the freeriding? From the map consumer's perspective there is no difference. And from the original creator's perspective it has a little accuracy cost. I think you are implying copyright laws.
Deliberate traps are a modern 20th-century practice. In old times, each mapmaker made their own innocent mistakes, and those mistakes got copied into other mapmakers' work. In other words, historians can plot "family trees" of maps, and figure out who copied from whom, based on which errors appear on which maps and on what date. Apparently California drawn as an island lasted about a century because engravers copied each other.
There were a few factors discouraging mapmakers from blatantly copying each other:
Rulers granted printing monopolies from the late 1400s. A mapmaker like Ortelius would print "cum privilegio" (lol) on a map. If you copied it, your expensive copper plates could be seized, along with a hefty fine.
Spain and Portugal each had a master chart which were state secrets because they held navigation knowledge worth a fortune. Portugal handed out severe punishments for leaking charts. (Leaks still happened: the Cantino planisphere was smuggled from Lisbon to Italy in 1502.)
Booksellers' guilds kept registers of who owned which work, and enforced it.
And lastly, copying meant hand-engraving a new copper plate, which took months of work and real skill. The practical barriers alone got rid of most of the copiers, but it didn't stop the big houses.
(I used Claude to research this fascinating rabbit hole, so I'm basically paraphrasing here. But this comment is my own original writing, so treat it as you like.)
I thought everybody knew this was a problem. Scrapers vacuum up data but don't have any way tell what's true or false.
It's interesting to have a specific case where obviously false data is being spread, but there's nearly an infinite number of more subtle variations around.
> Its made-up numbers have also ended up in an academic paper and in a bank's investment research.
It's an excellent way of showing how unreliable the information you find online is (and it will only get worse in the future), but willfully creating misinformation and made up shit and offering it as fact feels hostile to humanity.
Surely nobody got hurt as a consequence of those "facts", but bad decisions with real world impact could be made regardless. It feels like the behavior should be shunned in a general case (the same way I'd shun those numerous spam sites that pollute search results and very much would prefer they not be allowed to exist), even if in this case the intent was good.
Problem is, you can't really do anything about it, other than figuring out a way to take down hostile sites and keep them from popping up, so it's even worse than fraud or even just bad research in academia.
A little off-topic but I have been thinking about how writing changes in the age of AI.
I wonder if similar to how many engineers now write 90% or maybe 99% of code with AI and primarily weigh in on the plan/design, will writers too primarily weigh in only on the content brief/plan and then the actual post is seldom read in full (once you have faith that the AI is not making up citations). Because the audience of most writing is now bots prose and style are no longer relevant.
How surprisingly full-circle; AI is really becoming indistinguishable from humans! If you think about it, AI searching the internet is just like a human scrolling twitter. Maybe it's entertaining for them :)
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[ 3.0 ms ] story [ 29.8 ms ] threadHard disagree. Sometimes it's avoided, any other time we just go into cope mode. We discover on the ride down that the bottom isn't so bad. Turns out our collective control mechanisms didn't fail ourselves if the initial assessment was wrong.
if you just lie and an llm or a newspaper or a kid believe you, at that point it isn’t a criticism of their naivete, you are just lying
similarly hacking through systems or picking locks isn’t a showcase of weakness if systems, it’s just being a hacker
>It also hasn't happened because there is a reward for cheating. Scammers will label their phone numbers as the customer service number for your cable operator or bank. Counterfeiters will label their batteries or phone cases as official accessories. It's hard enough to get people to correctly label their stuff—but fact-checking billions of labels created by millions of people, none of whom you know personally or whose identities you can verify? That's insurmountable.
There's more he says—the preceding paragraph is below—but that line "there is a reward for cheating" is something I keep thinking about. It's hard not to see in so many of the discussions of LLMs.
>For one thing, there are irreconcilable differences in the ways in which things should be described. A site run by transphobes isn't going to label every story about a trans woman as "a story about a woman," irrespective of what the standard says. Microsoft is never going to label Excel as equivalent to Apple's Numbers, Google Sheets, and LibreOffice's Calc. Hermès is never going to list a $40,000 handbag as being made of the same material and having the same capacity as a $30 leather handbag of the same size and shape.
When an LLM is the lens through which you view that text, you often don't have the context of where it came from, and the LLM is often really bad at filtering trustworthiness based on context when retrieving information to answer a query with.
I wouldn't go to a random site with a sketchy URL to get the customer service number for my xyz. I'd go to their official domain. Thus successfully navigating the treacherous waters scammers attempt to muddy.
In principle AI Agents are superhuman at codeforces but tons of these vibe coded apps have ridiculously stupid performance problems.
The problem is a utilization of expertise thing, it can tell you with incredible detail about pros and cons and caveats to a problem if you ask about it. Truly superhumanly intelligent.
Calibration when it comes to actual decision making takes an incredible reduction in capability after that. Maybe it has something to do with the sycophancy or something but its like someone with exactly zero opinions (and therefore, taste).
My experience is that you can brainstorm with it on "Do A or B?" and it will produce a coinflip answer A, then if you continue and ask about a specific concern "what about this aspect of B?", it immediately 180s and flips back to B being way better than A.
Acting on this expertise when decision making points arise naturally for itself is where everything really nosedives. It's just gonna do whatever.
So when you are doing this "financial advice please" questioning, it is entirely hitting these weaknesses squarely in the face.
Gullible is an appropriate colloquialism here. As in they don’t check countervailing narratives naturally and fall into a recency sink on last context. Long walk context construction is hard, time consuming and most Importantly significantly more expensive.
The incentive will be to maintain a subscriber base.
Insanely optimistic view to take. I know a handful of people who make this effort in my personal life, including me, and we tend to be treated as paranoid for caring.
You have an entire lifetime of experience and the wisdom of countless generations to inform you of the context with which you should process new information. You were specifically trained for years to be able to do this somewhat effectively. Doing the same for an LLM may or may not be possible. They contain much of that experience in some form, but are not well equipped to make use of it iteratively. It is incredibly expensive to attempt even a simple and ineffective version of it with our current methods.
If the LLM can achieve even 90% accuracy, it becomes more of a hindrance than a help. Do you make use of the function and trust it successfully understood the provenance of new information? If so, ten percent of the time you will be misled, which could be disastrous across a population. If you don't simply trust it, then do you painstakingly check the provenance of everything it tells you? Of course not, you already don't do that with current search engines and blogs and such. Yet falling somewhere in between would be so much worse than simply not having the LLM attempt to discern trustworthiness from providence in the first place.
But all of that is opinion and I may be incorrect. Perhaps someone will create some ingenious way of imparting wisdom and discernment to LLMs next year and this will all be trivial.
With training data, however, no such annotation is possible.
During training? Not a chance.
The padlock on browsers means nothing to most humans. Permission popups are just there to be immediately accepted so that one can start scrolling reels sooner rather than later.
Billions have been spent on it.
Try searching Bing sometime. First result for Firefox is a malware clone site (you’ll have to scroll past the malware ad to see it).
(I am not including AI results in this).
Yes, but if you read this "semantic" info directly from a site, you can judge the site and its potential biases, and decide for yourself "this sells Louis Vuitton, of course they will praise their handbags". Or to pick a more extreme example that nobody would ignore, if you somehow land on an extremist website spewing racist crap, your alarms will surely trigger by anything they say.
But through an LLM this gets whitewashed and presented as authoritative and "neutral", when it isn't. You might know not to trust the LLM either (if you're paying attention; we all have our unconscious biases) but I bet you most people will be caught off guard.
The real lesson is this: trust in the source is the most important thing for 99.99% of what you read. You will sometimes read about facts that you can actually verify, but those are mostly useless in the grand scheme - except as a sanity check on the source. For everything else, you have to ultimately trust the source.
Now sure, sometimes you can get lucky and detect certain internal inconsistencies that prove that something can't be true. And to some extent, reading the same information from multiple sources you don't normally trust too much can be a slight signal that it might be true. But these are generally in the minority.
Again I am not ignoring reality. But for society to work and for us to live our daily lives with any semblance of sanity, we can't be expected to be fully informed on everything we see, touch, consume, etc. We have to be able to make reasonable assumptions about safety and accuracy or we can't function. And the time it takes to vet stuff is often a privilege (or you can brute force pay your way to safety).
I do not rigorously audit everything I read on wikipedia. I assume a certain baseline of accuracy up to a point, with of course exceptions. Do you pull up 3-4 high quality sources for everything you read or claim? Are you that academic about it?
Also, I didn't say you need to access high quality sources for everything, I again kind of said the opposite: you choose who to trust for what, and that's that. There is nothing more, and has never been anything more, that you can do. Sometimes people and sources you trust will still lie to you, or be wrong, and you'll know wrong information - this is also unavoidable.
Of course, the more important some information is to you, the more effort you should actually spend trying to verify with multiple trusted sources. Some weird fact about cats you saw in a TikTok? Fine to just believe it. Trying to figure out who to vote for, or if the elections were stolen? Probably worth checking a few more sources. Read some definition for a word on Wikipedia, or some old historical event? Probably fine. Reading about an even slightly controversial topic? Check the actual sources, don't trust the snapshot you happen to see*.
Of course, the way LLMs work is antithetical to this, as information provided directly by the LLM is not coming from any human source you can decide if you should trust or not. So you shouldn't trust it for anything important, unless you can verify. But for information or disinformation on the web, nothing has changed, except maybe the quantities.
*for a silly example: the exact definition of the word "electrocution" is a slightly controversial topic - whether electrocution refers to any (significant) injury caused by electricity, or only death. Pedants soemwaht regularly edit the article on Wikipedia to remove the "or serious injury" part of the definition, despite it carrying a link to the OED that includes this mention.
Now, the bigger problem happens if some of the trusted institutions and people go bad, for various reasons. If the NYT got bought out by Alex Jones, it might start mixing in lots of slop - but this is also a pre-existing problem.
How can this be fixed? Nothing prevents you from publishing straight BS, and LLMs will use any site that doesn't have a garbage reputation as a valid third-party source.
Of course, LLMs already use Reddit, Facebook and co. as a source, so it doesn't really matter...
As a human, this is fantastic.
> How can this be fixed? Nothing prevents you from publishing straight BS, and LLMs will use any site that doesn't have a garbage reputation as a valid third-party source.
Personally, I hope it can't, or the fix is onerous and kneecaps a lot of the utility of AIs.
I wonder if we can even have some adversarial competition, where competing businesses try to get AIs to ingest bad data to pollute their competitors decision-making.
For starters: lets get some fake math papers out there. Given proof-checking systems, maybe they won't totally stop OpenAI, but hopefully the bad math could at least drive up the cost and slow AI down.
s/human/unscrupulous marketer/
Bad for humans in general, good for hucksters and con artists. Net negative for humanity.
Furthermore, you are content to pretend that the rest of humanity is capable of doing this on a regular basis. And not just the top 10% of those who can read, interpret and weigh biases, but a solid portion of it.
It is a completely reasonable interpretation if you are an accelerationist living in the elite classes. In fact, perhaps, they are even counting on a majority of people thinking this is a fine environment.
I don't have the answers, but a lot of this is just a continuation of a very bad trend.
Regardless of how the data is sourced, this is bad. It's poisoning the internet.
Besides, such issues come from a lack of due diligence, which have only been exacerbated by AI.
What.. does Viagra have to do with this?
It's a ton of effort and expense, but probably feasible from a technical point of view. So instead, the model providers will probably do something silly like wire up rules like "NYT=Reliable;my.blog.forbes.com=Unreliable" and they'll say the issue is solved.
Defer to authority and trust your government to rank what is truth and what is not, so your LLM always gives you the right answer. Nothing can go wrong here ever.
People stop trusting what they read on the internet.
All alternates are just attempting to improve the baseline trustworthiness of random information from the internet (and/or LLM that's baby-birding it back to you). Any increase in how much people blindly trust random stuff from the internet only serves to increase the blast radius when it is wrong.
And there, in five words, is the problem/feature/definition of AI.
You can find people complaining that Google search results decayed to being unusable prior to LLMs being a big thing. The marketers and spammers effectively won that fight. They'll presumably win this one too.
1. This is a 2022 era picture that LLMs just suck up what's on the Internet and regurgitate it. It hasn't been true for a long time.
2. There's almost no one who has a stronger incentive to distinguish LLM generated text from non-generated LLM text. Training classifiers is pretty manageable.
if anything it’s more true now, AI chatbots rarely use raw LLM output, instead they have CoT and use search tool calls. and the speed at which webpages are crawled is in the matter of hours. I posted something here on hn, then searched for it on google 1 hr later and its AI answered with my comment
https://share.google/aimode/PkQUVfA68AlHGb7Yr
there’s several companies right now making a killing of organic placement on chatbots similar to the SEO rush of early google
> 2) There's almost no one who has a stronger incentive to distinguish LLM generated text from non-generated LLM text. Training classifiers is pretty manageable
i think it’s pretty naive to think that if LLM companies would develop LLM detection they would use it to skip training on that content.
instead, they use it to train their output to be less detectable.
LLM detection is not solvable because it is adversarial, an arms race
When I ask ChatGPT certain questions, it very clearly shows me the sources it's searching through in the form of favicons, and they're always the favicons of sites that are in the first 5-10 results for a Google search query that would fit what I've asked the LLM. It will then cite those websites as it's source, with a direct link to the page that also shows up in an equivalent Google search. It does that daily.
On the other hand, I recently asked ChatGPT for some information about a group of people, and the headshots it put with each summary were very clearly not the people it was describing. So hallucinations do still occur even on the latest models, it just seems unlikely that the favicons would be LLM-generated.
The other day for me Google search AI cited triggerbites which is a vibe data website.
Now, the model slips it in without any verification and casually builds on top of it.
Even if you are in the minority who asks for sources and truly verifies them, they are often made up (don’t exist) or don’t say what the LLM said they said.
Is this information on this website true/high quality?
We need a google for LLMs!
Oh, but LLMs don't consume ads.
We need ads for LLMs!
Help.
Google came and worked wonderfully for a year or two, until people learned to game page rank algorithm. Soon first google pages were just SEO spam and now half of the google is just spam, big corp websites, etc.
All of that precious stuff from small hobby websites about cool things to blogs that used to be wonderful read like https://www.joelonsoftware.com.
AI were not hurt that much by spam, I guess there were at least some curation done, the question is if this curation will work and for how long. It seems we are throwing the towel here as in case of search.
Backlink ranking was/is a reliable heuristic to answer that question across virtually all topics.
So I disagree that the "problem was not about quality."
Spam/scammers will always be in whatever landscape there is, and I agree part of what google does it keep them at bay.
> "Citogenesis" redirects here; not to be confused with cytogenesis.
I had to read that three times. I thought it was a joke at first.
This type of behavior is why gatekeeping institutions exist. We all pay a non-trivial cost because someone thinks that other people taking your lies seriously is somehow remarkable. It's not.
Research is hard work. Publishing fake research is just intentionally throwing wrenching into the machine of academia.
https://en.wikipedia.org/wiki/Trap_street
Other than some sort of social punishment I don’t see the cost for the copier. For the original author there is a cost of making your map less accurate on purpose.
There were a few factors discouraging mapmakers from blatantly copying each other:
Rulers granted printing monopolies from the late 1400s. A mapmaker like Ortelius would print "cum privilegio" (lol) on a map. If you copied it, your expensive copper plates could be seized, along with a hefty fine.
Spain and Portugal each had a master chart which were state secrets because they held navigation knowledge worth a fortune. Portugal handed out severe punishments for leaking charts. (Leaks still happened: the Cantino planisphere was smuggled from Lisbon to Italy in 1502.)
Booksellers' guilds kept registers of who owned which work, and enforced it.
And lastly, copying meant hand-engraving a new copper plate, which took months of work and real skill. The practical barriers alone got rid of most of the copiers, but it didn't stop the big houses.
(I used Claude to research this fascinating rabbit hole, so I'm basically paraphrasing here. But this comment is my own original writing, so treat it as you like.)
Trap Street - https://news.ycombinator.com/item?id=35768306 - May 2023 (29 comments)
Trap Street - https://news.ycombinator.com/item?id=17738847 - Aug 2018 (21 comments)
Trap street - https://news.ycombinator.com/item?id=4992748 - Jan 2013 (22 comments)
South Pacific Sandy Island 'proven not to exist' - https://news.ycombinator.com/item?id=4818584 - Nov 2012 (46 comments)
Not a Word: The story of Lillian Virginia Mountweazel - https://news.ycombinator.com/item?id=1504126 - July 2010 (10 comments)
It's interesting to have a specific case where obviously false data is being spread, but there's nearly an infinite number of more subtle variations around.
It's an excellent way of showing how unreliable the information you find online is (and it will only get worse in the future), but willfully creating misinformation and made up shit and offering it as fact feels hostile to humanity.
Surely nobody got hurt as a consequence of those "facts", but bad decisions with real world impact could be made regardless. It feels like the behavior should be shunned in a general case (the same way I'd shun those numerous spam sites that pollute search results and very much would prefer they not be allowed to exist), even if in this case the intent was good.
Problem is, you can't really do anything about it, other than figuring out a way to take down hostile sites and keep them from popping up, so it's even worse than fraud or even just bad research in academia.
I wonder if similar to how many engineers now write 90% or maybe 99% of code with AI and primarily weigh in on the plan/design, will writers too primarily weigh in only on the content brief/plan and then the actual post is seldom read in full (once you have faith that the AI is not making up citations). Because the audience of most writing is now bots prose and style are no longer relevant.