this post was submitted on 22 Aug 2026
305 points (97.8% liked)
Technology
87390 readers
3818 users here now
This is a most excellent place for technology news and articles.
Our Rules
- Follow the lemmy.world rules.
- Only tech related news or articles.
- Be excellent to each other!
- Mod approved content bots can post up to 10 articles per day.
- Threads asking for personal tech support may be deleted.
- Politics threads may be removed.
- No memes allowed as posts, OK to post as comments.
- Only approved bots from the list below, this includes using AI responses and summaries. To ask if your bot can be added please contact a mod.
- Check for duplicates before posting, duplicates may be removed
- Accounts 7 days and younger will have their posts automatically removed.
Approved Bots
founded 3 years ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
Considering the vast amounts of knowledge it has at its disposal, I can only conclude that it’s not very smart at applying it. A person with a fraction of that knowledge will produce better results.
So it has more access to information, but the results are poor compared to a person.
The important thing to remember is that it actually has zero access to information, because that's not how LLMs work.
At their core, they're vector databases, and they're trying to probabilistically come up with the next most likely token in a stream of tokens found in the DB. You can manipulate the stream by injecting text such as the content of existing files (which becomes more tokens) into the stream, but it never actually understands any of it.
That's why hallucinations are inherently unavoidable. It's really all just hallucinations. It's just that you can sometimes get useful text from their hallucinations if they happen to comport with reality.
Well, vector fields are information. But they have no understanding. The number 1 might be followed by 2 in 99.999% of cases, but it has no function to explain why, or to contextualize a scenario where that might be wrong.
They're information, but not the same information that was used to create them.
I’ve never done drugs though…
This is crazy lol they obviously have access to information.
You are both right. An LLM inherently has access to stuff the same way a brain in a jar has access to stuff. It's information comes from fine-tuning the models to return syntax that agent code can interpret as a request to invoke a tool. That tool returns information to the context of the conversation. It doesn't learn and it can't truly remember things. Every time you start a session it is brand new. It sees your codebase for the first time every time.
The information access they have is whatever the agent allows it to access via tool exposure. Be it built in tools, or MCP servers
As I've mentioned elsewhere, not if by "information" you mean semantic content that a mind can process. What they have are vector fields (essentially just numbers) with statistically more or less likely relationships.
If I say, "take me out to the ballgame" to an LLM, the tokens representing the words in the next verse of the song are statistically "close" in the vector database, so it's likely to generate them. But that doesn't mean it actually knows the lyrics... or even has those lyrics recorded in a regular database anywhere.
That's why they hallucinate. The model determines that the next token is something nonsensical, but it has no way of understanding that it has made a mistake. In a sense, it actually hasn't made a mistake. It's done exactly what it's designed to do. It's just that in the case of hallucinations, its output isn't useful.
Ypu have no idea what youre talking about. They absolutely have access to "information"
No, they really don't. That's not how they work. At least, not if the "information" you're talking about is real semantic content that real minds can process.
Every piece of information you think an LLM has access to is actually just converted into a stream of additional tokens that are fed into the model to (hopefully usefully) modify the next tokens it predicts. That's not the same thing as having actual access to information. Tokens are just numbers with statistically more (or less) likely relationships to each other.
I'm not trying to downplay LLMs. They're architecturally interesting and have genuine uses. I'm just trying to head off a bit of technical inaccuracy.
Absolutely correct and well said. Until you give it a tool to call a websearch (in my case SearxNG), I occasionally break it out (local 27B model) when a search is pulling lots of AI slop, Spy vs Spy style. I make it give me references and it usually indicates a bad search (XY problem)
LLMs don't. There are tools that can fetch new information and then gets fed into the model as more tokens, but that's just a special case of what kescusay is saying about injecting text.