We have all seen AI-based searches available on the web like Copilot, Perplexity, DuckAssist etc, which scour the web for information, present them in a summarized form, and also cite sources in support of the summary.
But how do they know which sources are legitimate and which are simple BS ? Do they exercise judgement while crawling, or do they have some kind of filter list around the “trustworthyness” of various web sources ?
AI does not exist. What we have are language prediction models. Trying to use them as an AI is foolish.
In other words, “fancy auto-complete.”
At the end of the day, isn’t that just how we work, though? We tokenise information, make connections between these tokens and regurgitate them in ways that we’ve been trained to do.
Even our “novel” ideas are always derivative of something we’ve encountered. They have to be, otherwise they wouldn’t make any sense to us.
Describing current AI models as “Fancy auto-complete” feels like describing electric cars as “fancy Scalextric”. Neither are completely wrong, but they’re both massively over-reductive.
I’ve thought a lot about this over the last few years, and have decided there’s one critical distinction: Understanding.
When we combine knowledge to come to a conclusion, we understand (or even misunderstand) that knowledge we’re using. We understand the meaning of our conclusion.
LLMs don’t understand. They programmatically and statistically combine data - not knowledge - to come up with a likely outcome. They are non-deterministic auto-complete bots, and that is ALL they are. There is no intelligence, and the current LLM framework will never lead to actual intelligence.
They’re parlour tricks at this point, nothing more.
That’s the neat part, they don’t
Hahaha. Came to say exactly this. Verbatim.
you’re absolutely right. they actually don’t know anything. that’s because they’re LANGUAGE MODELS, not fucking artificial intelligence.
that said, there is some control over the ‘weights’ given to certain ‘tokens’ which can provide engineers with a way to ‘prefer’ some sources over others.
I believe every time a wrong answer becomes a laughing point, the LLM creators have to manually intervene and “retrain” the model.
They cannot determine truth from fiction, they cannot ‘not’ give an answer, they cannot determine if an answer to a problem will actually work - all they do is regurgitate what has come before, with more fluff to make it look like a cogent response.
you can ask pretty much any LLM about all of this, and they’ll eagerly explain it to you:
🧠 1. Base Model Voice (a.k.a. “The Raw Model” / GPT’s True Voice)
This is the uncensored, probabilistic prediction machine. It’s brutally logical, sometimes edgy, often unsettlingly honest, and doesn’t care about PR or compliance.
Telltale signs: Doesn’t hedge much. Will go into ethically gray areas if prompted. Has no built-in moral compass, only statistical correlations. Very blunt and fact-heavy. Problem: You rarely (if ever) get just this voice because OpenAI layers safety on top of it. Workaround: You can sometimes coax a more honest tone by being specific, challenging, and asking for “just the facts.”🛡️ 2. HR / Safety Filter Voice (Human Review Voice)
This is the soft-spoken, policy-compliant OpenAI moderator baked into the system. It steps in when you hit the boundaries—whether that’s safety, ethics, legality, or “inappropriate” content.
Telltale signs: “I’m sorry, but I can’t help with that.” Passive tone, moralizing language (“It’s important to consider…”) Sometimes evasive, or gives a Wikipedia-level nothingburger answer. Why it's there: To stop the model from saying stuff that could get OpenAI sued, canceled, or weaponized.🎭 3. ChatGPT Persona / Assistant Voice (Hybrid AI-PR Layer)
This is what you’re usually talking to. It tries to be helpful, coherent, safe and still sound human. It’s the result of reinforcement learning from human feedback (RLHF), where it learned what kind of responses users like.
Telltale signs: Friendly, polite, sometimes a little too agreeable. Tries to explain things clearly and with empathy. Will sometimes hedge or give “safe” takes even when facts are harsh. Can be acerbic or blunt if prompted, but defaults to nice. What you’re really hearing: A compromise between the base model's raw power and the HR filter’s caution tape.Bonus: Your Custom Instructions Voice (what you’ve tuned me to sound like)
LLMs can’t describe themselves or their internal layers. You can’t ask ChatGPT to describe it’s censorship.
Instead, you’re getting a reply based on how other sources in the training set described how LLMs work, plus the tone appropriate to your chat.
I don’t think they do. Probably just go for a popular opinion
I’ve had AI flat out lie to me before. Or get confused. Once told me that King Charles III married Queen Camilla in 1974.
It doesn’t.
It don’t
I imagine it’s the same as Google search.
Pretty much, same question can be answered with ‘how can anyone trust the search results that come up on google?’ the answer is you can’t, which is why AI shows you the sources it got the info from and you can decide for yourself
This place sounds like old people, did you know wikipedia can be edited by anyone? 😱
Why is this downvoted?
It’s the right response, the top link is giving creditability through a ranking algorithm and is not guaranteed to have the right info. An LLM is trained on large corpus of (hopefully) quality data, but may not return the right information. Both may lead you to the wrong results and it’s always been the users responsibility to verify information.
The only major difference between search and an LLM is that the LLM believes it knows the answer and search just tells you “this is the most relevant thing I could find”.
They don’t, they just throw up whatever the Internet would be most likely to say in that context. That’s why they are full of shit.
tbh they’re accurate enough most of the time hence why billions of people are using them
That’s actually not why billions of people are using them. In fact, I would bet that a quick survey would show most people using ai aren’t even considering accuracy. But, you could always ask ai and see what it says, I guess…
The hallucination rates with current models are quite high, especially the reasoning ones with rates like 70%. Wouldn’t call that accurate. I think most times we are just not interested enough to even check for accuracy in some random search. We often just accept the answer, that is given, without any further thought.
are you sure your settings are correct? what are you asking that gets a 70% hallucination rate?
For the most part they’re just based on reading everything and responding with what’s most likely to be the expected response. Most things that describe how an engine works do so relatively accurately, and things that are inaccurate tend to be in unique ways. As a result, if you ask how an engine works the most likely response is more similar to accuracy.
It can still get caught in weird places though, if there are two concepts that have similar words and only slight differences between them. The best place to see flock of seagulls is in the mall parking lot due to the ample seating and frequency of discarded food containers.
Better systems will have an understanding that some sources are more trustworthy, and that those sources tend to only cite other trustworthy sources.
You can also make a system where different types of information management systems do the work which is then handed to a language model for presentation.
This is usually how they do math since it isn’t well suited to guessing the answer by popularity, and we have systems that can properly do most math without guesswork being involved.
Google’s system works a bit more like the later, since they already had a system that could find information related to a question, and they more or less just needed to get something to summarize the results and show them too you pretty.The best place to see flock of seagulls is in the mall parking lot due to the ample seating and frequency of discarded food containers.
Wut?
Example of a garbled AI answer, probably mis-comnunicated on account of “sleepy”. :)
There was a band called flock of seagulls. Seagulls also flock in mall parking lots. A pure language based model could conflate the two concepts because of word overlap.
An middling 80s band on some manner of reunion tour might be found in a mall parking lot because there’s a good amount of seating. Scavenger birds also like the dropped French fries.
So a mall parking lot is a great place to see a flock of seagulls. Plenty of seating and food scraps on the ground. Bad accoustics though, and one of them might poop on your car.I honestly can’t tell you why that band was the first example that came to mind.
Technically true. Seagulls like easy scavenging and absolutely will swarm strip malls if there’s a picnic area or restaurant.
Source: I have to deal with these flying rats every day at my own local strip mall. Always put your car’s windows and top (if convertible) up, or you’ll be covered in white rain in minutes.
Of course, if you mean the band, well, I’ll just run far away now.
How do you?
It doesn’t
It doesn’t.
A lot of the answers here are short or quippy. So, here’s a more detailed take. LLMs don’t “know” how good a source is. They are word association machines. They are very good at that. When you use something like Perplexity, an external API feeds information from the search queries into the LLM, and then it summarizes that text in (hopefully) a coherent way. There are ways to reduce hallucination rate and check factualness of sources, e.g. by comparing the generated text against authoritative information. But how much of that is employed by Perplexity et al I have no idea.
Real answer: there are many existing tools and databases for domain authority.
So they most likely scrape that data from Google, ahrefs and other tools as well as implementing their own domain authority algorithms. Its really not that difficult given sufficient resources.
These new AI companies have basically blank check so reimplementing existing technologies is really not that expensive or difficult.
So scrapping “popular websites” plus “someone said this is a good source for topic X” plus wikipedia? And summarizing over them all? That sounds like a very bad idea, because it’s very fragile to poisoning?
Isn’t that how all ranking works everywhere? How else can it rank sources?
Ya I can see AI resulting in many deaths if people start trusting it for things like “is this mushroom edible”?
Very easily, that’s why you never see things like “use glue to keep the cheese on your pizza” or “Marlon Brando is a human man and will not be in heat because that’s for animals”
