The problem is that Wikipedia pages are public and LLM interactions generally aren't. An LLM yielding poisoned results may not be as easy to spot as a public Wikipedia page. Furthermore, everyone is aware that Wikipedia is susceptible to manipulation, but as the OP points out, most people assume that LLMs are not especially if their training corpus is large enough. Not knowing that intentional poisoning is not only possible but relatively easy, combined with poisoned results being harder to find in the first place makes it a lot less likely that poisoned results are noticed and responded to in a timely manner. Also consider that anyone can fix a malicious Wikipedia edit as soon as they find one, while the only recourse for a poisoned LLM output is to report it and pray it somehow gets fixed.
Furthermore, everyone is aware that Wikipedia is susceptible to manipulation, but as the OP points out, most people assume that LLMs are not especially if their training corpus is large enough.
I'm not sure this is true. The opposite may be true.
Many people assume that LLMs are programmed by engineers (biased humans working at companies with vested interests) and that Wikipedia mods are saints.
I don't think anybody who has seen an edit war thinks wiki editors (not mods, mods have a different role) are saints.
But a Wikipedia page cannot survive stating something completely outside the consensus. Bizarre statements cannot survive because they require reputable references to back them.
There's bias in Wikipedia, of course, but it's the kind of bias already present in the society that created it.
I don't think anybody who has seen an edit war thinks wiki editors (not mods, mods have a different role) are saints.
I would imagine that fewer than 1% of people who view a Wikipedia article in a given month have knowingly 'seen an edit war'. If I'm right, you're not talking about the vast majority of Wikipedia users.
But a Wikipedia page cannot survive stating something completely outside the consensus. Bizarre statements cannot survive because they require reputable references to back them.
This is untrue. There are several high profile examples of false information persisting on Wikipedia:
Wikipedia’s rules and real-world history show that 'bizarre' or outside-the-consensus claims can persist—sometimes for months or years. The sourcing requirements do not prevent this.
- (Nobel-winning) novelist Philip Roth publicly complained that Wikipedia refused to accept his correction about the inspiration for The Human Stain until he published an *open letter in The New Yorker*. The false claim persisted because Wikipedia only accepts 'reliable' secondary sources: https://www.newyorker.com/books/page-turner/an-open-letter-t...
Isn't the fact that there was controversy about these, rather than blind acceptance, evidence that Wikipedia self-corrects?
If you see something wrong in Wikipedia, you can correct it and possibly enter a protracted edit war. There is bias, but it's the bias of the anglosphere.
And if it's a hot or sensitive topic, you can bet the article will have lots of eyeballs on it, contesting every claim.
With LLMs, nothing is transparent and you have no way of correcting their biases.
I think only very obscure articles can survive for that long, merely because not enough people care about them to watch/review them. The reliability of Wikipedia is inversely proportional to the obscurity of the subject, i.e. you should be relatively safe if it's a dry but popular topic (e.g. science), wary if it's a hot topic (politics, but they tend to have lots of eyeballs so truly outrageous falsehoods are unlikely), and simply not consider it reliable for obscure topics. And there will be outliers and exceptions, because this is the real world.
In this regard, it's no different than a print encyclopedia, except revisions come sooner.
It's not perfect and it does have biases, but again this seems to reflect societal biases (of those who speak English, are literate and have fluency with computers, and are "extremely online" to spend time editing Wikipedia). I've come to accept English Wikipedia's biases are not my own, and I mentally adjust for this in any article I read.
I think this is markedly different to LLMs and their training datasets. There, obscurity and hidden, unpredictable mechanisms are the rule, not the exception.
Edit: to be clear, I'm not arguing there are no controversies about Wikipedia. I know there are cliques that police the wiki and enforce their points of view, and use their knowledge of in-rules and collude to drive away dissenters. Oh well, such is the nature of human groups.
but again this seems to reflect societal biases (of those who speak English, are literate and have fluency with computers, and are "extremely online" ...)
I don't believe that Wikipedia editorial decisions represent a random sample of English speakers who have fluency with computers.
Again, read what Larry Sanger wrote, and pay attention to the examples.
I've read Sanger's article and in fact I acknowledge what he calls systemic bias, and also mentioned hidden cliques in my earlier comment, which are unfortunately a fact of human society. I think Wikipedia's consensus does represent the nonextremist consensus of English speaking, extremely online people; I'm fine with sidelining extremist beliefs.
I think other opinions of Sanger re: neutrality, public voting on articles, etc, are debatable to say the least (I don't believe people voting on articles means anything beyond what facebook likes mean, and so I wonder what Sanger is proposing here; true neutrality is impossible in any encyclopedia; presenting every viewpoint as equally valid is a fool's errand and fundamentally misguided).
But let's not make this debate longer: LLMs are fundamentally more obscure and opaque than Wikipedia is.
People think I'm crazy for saying this but the only thing stopping big corporations from hiring hitmen to just actually murder people to be more profitable is that it's illegal to do so. If it were legal for them to make you put your baby in overhead luggage you bet your ass they'd be doing it if it were profitable.
99% of my LinkedIn feed these days is AI slop from people and companies I have never heard of. IDK why anyone looks at the anything other than job listings on LinkedIn. Everything else is garbage.
Or perhaps there just happened to be an overlap between the nonsense they believe in and some shred of truth that you have to squint really hard to make work.
Which is why AI therapy scares me so much, because my insurance doesn't care about the quality of results at all and wold love to replace all therapy with AI to save a buck.
To me the biggest problem with AI therapy is that LLMs (at least all the big ones rn) basically just agree with you all the time. For therapy this can just reinforce harmful thoughts rather than pushing back against them. I do an exercise every now and then where I try to see how long it takes ChatGPT to agree with me that I am God Incarnate and that I created the Universe and control everything within it. It has never taken me more than 5 messages (from me) to get to that point even if I start on pretty normal topics about spirituality and religion, and I don't use any specific tricks to try to get past filters or anything, I just talk as if I truly believe this and make arguments in favor, and that makes ChatGPT agree with me on this absurd and harmful delusion. I don't understand how anyone can possibly recommend AI therapy when the possibility of reinforcing delusions this harmful exists. These things are agreement machines, not therapy machines.
The main problems I see are that we don't clean the insides of computer parts and we can drink water with way more calcium in it than is good for high temperature water heaters. Cooling water needs to be treated to not grow algae and bacteria in it, and a lot of times that renders it poisonous to drink. Conversely, drinking water has a lot of minerals still in it and those minerals will deposit and form scale on the insides of your heat transfer surfaces, which will severely impact performance over time. It may not even take that long depending on how hot the surface is and how hard your water is.
There should not be any contact between your heat source and your drinking water, in pretty much any modern water-heating system. You heat a refrigerant, then run it and the water through opposite sides of a heat exchanger (most domestic hot water tanks have a coil-style heat exchanger inside).
Kagi is nice but it just seems so expensive for what it is. I get that search that actually shows me what I want is expensive but I would want to use this as a family plan and I think we would go through the lower paid tiers pretty quickly.
