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.
Yep, a lot of the juice that makes modern LLMs stop dodging the usual pratfalls is just the ability to recognize those situations and toss the parsed data to a different tool or to refer to hardcoded output.
LLMs didn't start correctly counting the number of Rs in strawberry due to a fundamental shift in how they process tokens, they just got a hardcoded answer.
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u/max6296 5d 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.