Like, it literally creates answers out of thin air then sells it as if it's correct. It doesn't even try to get it right. What sort of redundancy is there in analyzing if the answer is correct before spewing it out? I thought LLMs were supposed to discern what the best answer is given what was said to it based on its training, yet it'll give answers that don't exist based on any training. It's not like it learned the wrong answer from a Reddit post and just posted what Reddit said. It legit is making up wrong answers then citing correct answers. It just outright gets it wrong almost on purpose.
Anyone understand why LLMs fail so much?
I understand they run correlations but how does it determine a wrong answer is the most correlated to the correct response given the prompt instead of the actual correct answer...
Explain how they are able to do math and spatial reasoning.
ecognition: LLMs can often reproduce arithmetic or algebraic manipulations if they appear frequently in the training data. For example, they can compute 2 + 3 = 5 or symbolically solve simple linear equations.
When prompted to “show your work,” LLMs can sometimes emulate a logical sequence of steps in a calculation, mimicking the kind of reasoning a human might write down.
LLMs can recall formulas, rules, and common mathematical facts that they are trained on.
But LLMs do not “compute” numbers in the way a calculator does. They generate numbers based on patterns, so mistakes accumulate with larger numbers or complex operations. For example, asking it to compute 234 * 567 may result in a wrong number because the model predicts what looks plausible rather than calculating precisely.
If it tries to break it down into a multi-step process, these are immensely error-prone, as the model doesn’t track intermediate results reliably.
Anything that requires abstraction, they will struggle with. Examples like proofs, higher-dimensional algebra, and precise symbolic manipulations.
This is because LLMs encode statistical correlations between tokens. They don’t internally maintain the concept of a number as a manipulable object, they only know how numbers “look” in context.
It doesn know that 2 is 2, it just knows that 2 comes after 1, turns 10 into 12 and so on and so on.
Spatial reasoning often requires a continuous, structured mental model (a 3D coordinate system). LLMs operate in a discrete token space, which is poorly suited for inherently geometric problems. They can simulate reasoning through learned text patterns but cannot “visualize” in the human sense.
They can understand and generate text describing spatial relationships, like “The cup is on the table, and the book is next to it.” For example, “left of,” “right of,” “above,” “below” can be tracked in sequences reasonably well.
LLMs lack an internal geometric or visual model of space. They cannot mentally rotate objects, imagine perspectives, or simulate physics accurately. They also cannot reliably generate or manipulate grids, matrices, or plots without structured guidance.