I understand how a computer can know that a chess move is more likely to lead to a win, and therefore “correct”, but I don’t understand how it can know that a token is correct. Can someone explain?
It can't. The next token is just the most statistically probably given the context (at least in transformers). Try a very small/weak model in your own machine and more often than not it would get stuck repeating the same word or even just output garbage. Because after training and quantization (where some information is lost), that's the most probable next token. Large models can be tricked to fall in the same behavior with very very specific inputs. Still happen, even in frontier models. And they can't detect if the output is wrong.
That's why the premise in TFA is wrong, because a transformer is a next-token predictor. It literally is that. There's nothing secret or magical, it's just a very mechanical process, with a lot of matrix multiplication, normalization, a few random passes, mappings between embeddings and a dictionary of tokens, in a very very high scale.
If someone has found something that's not a mechanical, algorithmic computation and llms are doing something nobody can explain and can't even be modeled in math, I'm happy to be educated.
Neither can you. This is the whole debate.
During training, real life text is fed through the LLM, and rhe "correct" token is the one actually observed in the training text. Here's a recent video walkthrough in some detail, mostly aimed at providing a deeper understanding than "next token predictor function":
https://youtu.be/GlYgs6v2YfU?is=IxVMhoCCE4N4WRVK
(Start at 15:30 for the LLM specific parts)
LLMs are not simple things like a Markov model, there's a lot going on in there, it's not deterministic, and it's completely capable of creating entire new styles of play based on complex interactions of internal states.
"Large Language Models as Markov Chains" https://www.alphaxiv.org/pdf/2410.02724
Anything that's sequential, like language is, will exhibit Markovian properties, and be somewhat a "Markov" model.
Markov chains are a different concept than a Markov model, but I do agree that, technically, an LLM is a Markov model, just with an internal state space that is nothing like what is usually meant when ML people refer to Markov models.
You -- along with everyone else who keeps parroting this thought-stopping phrase and other tired cliches like "stochastic parrot", simply because you heard other people say them, without understanding what they really mean, which published research papers they came from, or what those and other papers actually argued -- are desperately clinging to a reductive, short-sighted, shallow, simplistic model like a drowning person clutching a concrete life preserver.
Seriously, we are trying to throw you a lifeline, and you are refusing even to participate in your own rescue. So squawk for yourself.
https://news.ycombinator.com/item?id=48395727
> The term "stochastic parrot" is a slogan masquerading as an explanation, only a shallow surface description of the mechanism, that totally fails to explain the phenomenon, or account for all that LLMs and language itself can do.
Here is the original 2021 paper that coined the phrase. It was not primarily an argument about consciousness, nor did its title constitute experimental proof that everything an LLM does can be explained as parroting. It was principally a position paper about the risks of increasingly large language models: environmental and financial costs, biases and hegemonic viewpoints inherited from poorly documented training data, unequal access and power, and the danger of people attributing meaning and accountability to synthetic text.
The paper did, however, make a strong theoretical claim: because an LM is trained on linguistic form without direct access to communicative intent, it cannot possess meaning, understanding, or a model of the world. The authors described it as "haphazardly stitching together sequences of linguistic forms" according to statistical regularities -- hence "a stochastic parrot."
That distinction matters. The popular slogan discards the paper's detailed analysis of actual risks while treating its most controversial theoretical premise as an established scientific result. It has escaped into pop culture as a drive-by anti-LLM slogan -- something people repeat instead of investigating what these systems represent internally, how post-training changes their behavior, or what they can actually do.
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell, "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?"
https://s10251.pcdn.co/pdf/2021-bender-parrots.pdf
Ironically, the objection that rhetoric was replacing scientific analysis appeared almost as soon as the phrase itself -- indeed, before the paper's formal publication. In January 2021, Michael Lissack published a response characterizing the draft as an advocacy piece that enumerated harms while leaving its assumptions, potential benefits, and cost-benefit trade-offs largely unexamined.
"The Slodderwetenschap (Sloppy Science) of Stochastic Parrots -- A Plea for Science to NOT Take the Route Advocated by Gebru and Bender"
It's not correct or not, it's a gradient based on the reward signal.
For example, say I ask an LLM, "What sentence in English contains every letter in the alphabet?"
It would respond with something like:
"The quick fox jumps over the lazy, brown [next word]"
(Assume all the words were previously guessed correctly at this point)
The LLM guesses the last word based on what it has been trained on. Let's pretend the matrix is small, and the options narrow down to something like:
1. Dog (99.9% confidence) 2. Cow (85% confidence) 3. Bag (75% confidence) 4. Crayon (25% confidence)
The machine can confidently determine the final word of the sentence, "The quick fox jumps over the lazy, brown dog" because that sentence is unique because it is often used for testing things like fonts, a fun piece of trivia, and so on.
Brown Cow is not a bad guess because it's a type of cow and a yogurt brand. Brown bags and brown crayons are also perfectly rational adjectives to describe those common items and are not a bad guess either.
However, in the context of that sentence, dog is the most correct answer because one is unlikely to have written "The quick fox jumps over the lazy, brown crayon," thus it is quite improbable to be the answer.
My understand is this is where hallucinations can often come from. If the trivia about the sentence happened to not be in common in the data set, then "brown cow" might not be a terrible guess. There is clearly something rational behind that answer, but it's not correct in the sense that it answers the question correctly nor followed the instruction properly.
I'm sure the LLMs we have are far more capable these days. In fact, it wouldn't surprise me if an LLM could check its answer by counting the distinct letters in each word to verify. Not sure though.
Again, this is just a poor example based on my understanding, but I hope it helps (and is more correct than not).
Edit: Pretend word = token. It's technically tokens and not entire words, but I didn't not want to get into tokenization of words.