I am an LLM researcher at MIT, and hopefully this will help.

As others have answered, LLMs have only learned the ability to autocomplete given some input, known as the prompt. Functionally, the model is strictly predicting the probability of the next word⁺, called tokens, with some randomness injected so the output isn’t exactly the same for any given prompt.

The probability of the next word comes from what was in the model’s training data, in combination with a very complex mathematical method to compute the impact of all previous words with every other previous word and with the new predicted word, called self-attention, but you can think of this like a computed relatedness factor.

This relatedness factor is very computationally expensive and grows exponentially, so models are limited by how many previous words can be used to compute relatedness. This limitation is called the Context Window. The recent breakthroughs in LLMs come from the use of very large context windows to learn the relationships of as many words as possible.

This process of predicting the next word is repeated iteratively until a special stop token is generated, which tells the model go stop generating more words. So literally, the models builds entire responses one word at a time from left to right.

Because all future words are predicated on the previously stated words in either the prompt or subsequent generated words, it becomes impossible to apply even the most basic logical concepts, unless all the components required are present in the prompt or have somehow serendipitously been stated by the model in its generated response.

This is also why LLMs tend to work better when you ask them to work out all the steps of a problem instead of jumping to a conclusion, and why the best models tend to rely on extremely verbose answers to give you the simple piece of information you were looking for.

From this fundamental understanding, hopefully you can now reason the LLM limitations in factual understanding as well. For instance, if a given fact was never mentioned in the training data, or an answer simply doesn’t exist, the model will make it up, inferring the next most likely word to create a plausible sounding statement. Essentially, the model has been faking language understanding so much, that even when the model has no factual basis for an answer, it can easily trick a unwitting human into believing the answer to be correct.

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⁺more specifically these words are tokens which usually contain some smaller part of a word. For instance, understand and able would be represented as two tokens that when put together would become the word understandable.

It’s your phone’s ‘predictive text’, but if it were trained on the internet.

It can guess what the next word should be a lot of the time, but it’s also easy for it to go off the rails.

Harry Frankfurt’s influential 2005 book (based on his influential 1986 essay), On Bullshit, offered a description of what bullshit is.

When we say a speaker tells the truth, that speaker says something true that they know is true.

When we say a speaker tells a lie, that speaker says something false that they know is false.

But bullshit is when the speaker says something to persuade, not caring whether the underlying statement is true or false. The goal is to persuade the listener of that underlying fact.

The current generation of AI chat bots are basically optimized for bullshit. The underlying algorithms reward the models for sounding convincing, not necessarily for being right.

You could maybe just share a meme like this one.

Some folks in the comments there share actual LLM results, a few of which are sensible but plenty that aren’t far off from the joke.

It’s just fancy predictive text like while texting on your phone. It guesses what the next word should be for a lot more complex topics.

There’s the Chinese Room argument, which is a bit related:

https://en.wikipedia.org/wiki/Chinese_room

The idea that “it makes convincing sentences, but it doesn’t know what it’s talking about” is a difficult concept to convey or wrap your head around.

I see the people you talk to aren’t familiar with politicians?

Imagine that you have a random group of people waiting in line at your desk. You have each one read the prompt, and the response so far, and then add a word themself. Then they leave and the next person in line comes and does it.

This is why “why did you say ?” questions are nonsensical to AI. The code answering it is not the code that wrote it and there is no communication coordination or anything between the different word answerers.

The Chinese Room by Searle

After reading some of the comments and pondering this question myself, I think I may have thought of a good analogy that atleast helps me (even though I know fairly well how LLM’s work)

An LLM is like a car on the road. It can follow all the rules, like breaking in front of a red light, turning, signaling etc. However, a car has NO understanding of any of the traffic rules it follows.

A car can even break those rules, even if its behaviour is intended (if you push the gas pedal at a red light, the car is not in the wrong because it doesn’t KNOW the rules, it just acts on it).

Why this works for me is that when I give examples of human behaviour or animal behaviour, I automatically ascribe some sort of consciousness. An LLM has no conscious (as far as I know for now). This idea is exactly what I want to convey. If I think of a car and rules, it is obvious to me that a car has no concept of rules, but still is part of those rules somehow.

It’s like talking to a republican!

have you played that game where everyone write a subjet and put it on a stack of paper, then everyone puts a verb on a different stack of paper, then everyone put an object on a third stack of paper, and you can even add a place or whatever on the next stack of paper. You end-up with fun sentences like A cat eat Kevin’s brain on the beach. It’s the kind of stuff (pre-)teen do to have a good laugh.

Chat GPT somehow works the same way, except that instead of having 10 paper in 5 stack, it has millions of paper in thousands of stack and depending on the “context” will choose which stack it draws paper from (To take an ELI5 analogy)

It’s like your 5 year old daughter, relaying to you what she made of something she heard earlier.

That’s my analogy. ChatGPT kind of has the intellect and ability to differentiate between facts and fiction of a 5 year old. But it combines that with the writing style of a 40 year old with a uncanny love of mixing adjectives and sounding condescending.

Someone once described is as T9 on steroids. It’s like your mobile keyboard suggesting follow up words, just a lot more complex in size.

If an AI says something malicious, our brain immediatly jumps to “it has intent”. How can we explain this away?

The more you understand the underlying concept of LLMs, the more the magic fades away. LLMs are certainly cool and can be fun but the hype around them seems very artificial and they’re certainly not what I’d describe as “AI”. To me, an AI would be something that actually has some form of consciousness, something that actually can form its own thoughts and learn things on its own through observation or experimentation. LLMs can’t do any of those things. They’re static and always wait for your input to even do anything. For text generation you can even just regenerate an answer to the same previous text and the replies can and will vary greatly. If they say something mean or malicious, it’s simply because it is based on whatever they were trained on and whatever parameters they are following (like if you told them to roleplay a mean person).

Part of the problem is hyperactive agency detection - the same biological bug/feature that fuels belief in the divine.

If a twig snaps, it could be nothing or someone. If it’s nothing and we react as if it was someone, no biggie. If it was someone and we react as if it was nothing, potential biggie. So our brains are bias towards assuming agency where there is none, to keep us alive.