A new study highlights a critical flaw in large language models (LLMs): their inability to reason mathematically. By making minor changes to math problems, researchers exposed how LLMs rely heavily on pattern recognition rather than true understanding. Even sophisticated models like OpenAI's o1-preview struggled significantly, dropping accuracy by as much as 17.5%. This raises serious questions about the reliability of AI in tasks requiring logical thinking and problem-solving.
None of this should be surprising. Such AI 'hallucinations' are a problem inherent to all large language models that nobody's solved yet, reckless when you consider that Apple engineers warned about the tech's gaping deficiencies. The yet-to-be-peer-reviewed work, which tested the mathematical 'reasoning' of some of the industry's top LLMs, added to the consensus that AI models don't actually reason. They attempt to replicate the reasoning steps observed in their training data.
Researchers tested these models' capabilities by presenting them with thousands of math problems from the widely used benchmark. A typical question is as follows: 'James buys 5 packs of beef that are 4 pounds each. The price of beef is $5.50 per pound. How much did he pay?' Some questions are a tad more complicated, but it's nothing that a well-educated middle schooler can't solve. The way the researchers exposed these gaps in the AI models was shockingly easy: they simply changed the numbers in the questions. This prevents data contamination — in other words, ensuring that the AIs haven't seen any of these exact problems before in their training data, without actually making the problems any harder. This alone caused a minor but notable drop in accuracy in every single of the 20 tested LLMs. But when the researchers took things a step further by also changing the names and adding in irrelevant details — like in a question about counting fruits, remarking that a handful of them were 'smaller than usual' — the performance drop was, in the researchers' own wording, 'significant between models, but even the cleverest of the bunch, OpenAI's o1-preview, plummeted by 17.5 percent. (Its predecessor GPT-4o, fell by 32 percent.)'This reveals a critical flaw in the models' ability to discern relevant information for problem-solving, likely because their reasoning is not formal in the common sense term and is mostly based on pattern matching,' the researchers wrote. Put another way, AI is very good at appearing smart, and will often give you the right answer! But once it can't copy someone's homework word-for-word, it struggles — big time. You'd think this would raise serious questions about trusting an AI model to regurgitate headlines — swapping words around without actually understanding how that changes the overall meaning — but apparently not. Apple knew about the serious flaws that every single LLM to date has shown and released its own model anyway. Which to be fair, is the modus operandi of the entire AI industry
AI Large Language Models Mathematical Reasoning Pattern Matching LLM Flaws
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