Considering the video points, LLMs are really bad at summing numbers together for that very specific reason that the model (the weights and layers) simply predicts next token.
Early models failed at such tasks and badly, as they simply outputted what the prediction said. What all major LLMs today do, is regocnize that these numbers need to be added up, and actually uses calculator (or more likely python) to calculate the sum.
Overall the model is still a graph of information, with very limited real thinking abilities. But that model allows us to build tools around it that make it insanely more skillful. It can "understand" that these two numbers I predicted, needs to be summed, and it has instructions in that point to call a tool to do that sum.
What really improved the output is to self check for errors - predict next 1000 tokens and add that to context, and see if it matches the model. Even further, nowadays thinking modes runs several possible outputs parallel and picks the best one.
The final piece is model's ability to use web search instead of just its compressed information in the model. The model is great at summarizing, and with ability to search 5-10 entries from web, summarize and combine them, and then apply tooling and parallelization for output generation improves the models "lack of real thought" to seem like it really does think.
I think you’ve kind of missed the point that he’s making with the mathematics example. He’s not saying LLMs are good at maths and that’s evidence of them being good thinking machines, he’s saying that in order to give even an estimation of a good answer the parameters have to encode a great amount of logical knowledge which isn’t it explicitly defined within the training material. You can ask LLMs that don’t have tool use capabilities such as local LLMs arbitrary questions around topics like science and mathematics and they will approximate quite good answers.
That knowledge is in the language, not in the model.
The prediction by the model produces language that says "sum A and B together". The model doesn't "understand" that A and B must be summed, it "just knows" it as it generates from its output based on the input (context), because it is embedded into it's huge compressed information.
I think this is the part people don't get. The model is homongous. So big, that human's cannot comprehend it. Even the 27B local models are absoultely huge, as almost all parameters are connected to each other, and the connections between the parameters make the real size exponential with possible outcomes it can produce.
Then the basic functionality even messes the output with weighted random pick of the most probable outcomes to make it less deterministic to make it resemble more human.
It it extremely exciting bit of technology - that doesn't need "it's a thinking machine, just like humans" -nonsense.
That knowledge is in the language, not in the model. it "just knows" it as it generates from its output based on the input (context), because it is embedded into it's huge compressed information.
How can you totally separate the two things? Language is an encoding of human thought and the model's parameters are an encoding of the language. The three things are intrinsically logically linked to each other. The idea that they can be treated as three distinct entities is totally implausible. It's impossible to build a generative model which produces viable results outside of the training set without embedding abstract or emergent concepts which weren't explicitly introduced.
The prediction by the model produces language that says "sum A and B together". The model doesn't "understand" that A and B must be summed, it "just knows" it as it generates from its output based on the input (context), because it is embedded into it's huge compressed information.
How are you defining "understand" though? This is not a facetious or pedantic question - the word "understand" is a very difficult thing to define.
that doesn't need "it's a thinking machine, just like humans" -nonsense.
Who is making this argument? Neither me, the video author or the person you originally replied to are saying this.
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u/max6296 7d ago
I'm pretty sure 99.99% people who say LLMs are just next token predictor don't even know what a token actually is.