They don't. That's why the summaries are almost always wrong or at least irrelevant. Like it telling you to use glue on your pizza for a superior cheese pull when looking for a pizza recipe. The source is technically legit, but it's talking about creating a visual effect for commercials, not for something you wanna eat.
That's the neat part, they don't
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.
Hahaha. Came to say exactly this. Verbatim.
They can’t. That’s why there’s glue on pizza.
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.
It doesn't.
I don't think they do
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."
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.
I don't use Google, but perhapas I should? You could make a bingo game out of finding funny summaries like that one.
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"
Most of the time if I read the AI summary from Google it's wrong. Very few times has it actually been helpful.
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.
It doesn't.
It doesn't
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?
Ya I can see AI resulting in many deaths if people start trusting it for things like "is this mushroom edible"?
Isn't that how all ranking works everywhere? How else can it rank sources?
My point is "summarizing over all of those" and "poisoning".
Source of category 1 says cheese is made from XYZ and yellow
Source from category 2 confirms 1 in different words and adds that it has holes
Source from category 3 confirms 2 and adds that its also blue, not only yellow
Source 4 talks about blue cheese only
Poisoning would mean that in the summary cheese is yellow with blue holes.
It don't
That's why I like Perplexity; I can just check the sources it used for accuracy. Unfortunately they have a garbage privacy policy, but I use a private DNS with good tracking filters so I'm only mildly concerned.
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?
!nostupidquestions is a community dedicated to being helpful and answering each others' questions on various topics.
The rules for posting and commenting, besides the rules defined here for lemmy.world, are as follows:
Rule 1- All posts must be legitimate questions. All post titles must include a question.
All posts must be legitimate questions, and all post titles must include a question. Questions that are joke or trolling questions, memes, song lyrics as title, etc. are not allowed here. See Rule 6 for all exceptions.
Rule 2- Your question subject cannot be illegal or NSFW material.
Your question subject cannot be illegal or NSFW material. You will be warned first, banned second.
Rule 3- Do not seek mental, medical and professional help here.
Do not seek mental, medical and professional help here. Breaking this rule will not get you or your post removed, but it will put you at risk, and possibly in danger.
Rule 4- No self promotion or upvote-farming of any kind.
That's it.
Rule 5- No baiting or sealioning or promoting an agenda.
Questions which, instead of being of an innocuous nature, are specifically intended (based on reports and in the opinion of our crack moderation team) to bait users into ideological wars on charged political topics will be removed and the authors warned - or banned - depending on severity.
Rule 6- Regarding META posts and joke questions.
Provided it is about the community itself, you may post non-question posts using the [META] tag on your post title.
On fridays, you are allowed to post meme and troll questions, on the condition that it's in text format only, and conforms with our other rules. These posts MUST include the [NSQ Friday] tag in their title.
If you post a serious question on friday and are looking only for legitimate answers, then please include the [Serious] tag on your post. Irrelevant replies will then be removed by moderators.
Rule 7- You can't intentionally annoy, mock, or harass other members.
If you intentionally annoy, mock, harass, or discriminate against any individual member, you will be removed.
Likewise, if you are a member, sympathiser or a resemblant of a movement that is known to largely hate, mock, discriminate against, and/or want to take lives of a group of people, and you were provably vocal about your hate, then you will be banned on sight.
Rule 8- All comments should try to stay relevant to their parent content.
Rule 9- Reposts from other platforms are not allowed.
Let everyone have their own content.
Rule 10- Majority of bots aren't allowed to participate here. This includes using AI responses and summaries.
Our breathtaking icon was bestowed upon us by @Cevilia!
The greatest banner of all time: by @TheOneWithTheHair!