Apple's Research Reveals LLMs Are More About Pattern Matching Than Reasoning

Apple’s latest research shows that LLMs aren’t as good at reasoning as the hype would have you believe. Through a controlled experiment with simple math problems, researchers found that LLMs may not truly solve problems. Instead, they seem to rely on patterns from their training data.

Apple’s Experiment Design and Key Findings

Apple’s research team created GSM-Symbolic, a new benchmark to test LLMs. It’s an upgrade to the common GSM8K (Grade School Math 8K) dataset. GSM-Symbolic lets researchers make controlled changes to math questions, like changing numbers or adding irrelevant details – testing across all state-of-the-art models. These seemingly minor changes introduced variability and errors reducing the models' performance by up to 65%, revealing that today’s LLMs aren’t really thinking through problems. Instead, they rely on matching patterns from training data. When the pattern doesn’t fit, the model becomes less reliable, suggesting that LLMs don’t handle variations well, even when the underlying problem is the same.

LLMs' Implications for High-Stake Applications

LLMs struggle to adapt to nuanced scenarios, posing risks in fields like finance, healthcare, and law where accuracy is mission-critical. Take JPMorgan, for example: relying only on an LLM to flag unusual transactions can lead to false positives or missed fraud cases. Apple’s research shows that as transaction details that get more complex, may cause the model’s accuracy to drop. So, the harder the scenario, the more likely the model is to misjudge. This makes additional human oversight essential in high-stakes fields.

These findings suggest that ‘agentic workflows’ (AI systems managing complex tasks alone) may be more hype than practical for now. Yes, specialized workflows can work within narrowly defined parameters with extensive training, much like a new employee trained for a specific task. However, these models aren’t designed to generalize across scenarios without direct, ongoing human support.

One way to get the most value from LLMs is to use them in a hybrid approach – using LLMs with monitoring and rule-based checks. That way, you benefit from their strengths without betting the house on their judgments.?

The Bottom Line

Despite the hype, AI isn’t ready to take over human jobs – or the world – just yet. LLMs work best when used for specific tasks, but expecting them to reason like humans is still a reach. Given the current state of the art of LLMs, we should use this technology to support human decision-making, not replace it.