No, LLMs Don’t Truly Reason — And That’s Okay
There’s been ongoing debate about whether large language models (LLMs) are capable of genuine logical reasoning.
Think back to the first time you used an LLM, whether it was ChatGPT, Meta AI, or another system. It likely felt like magic. As they've continued to develop, we've all seen how transformative these tools can be.
Yet, despite their impressive capabilities, these tools have significant limitations. Hallucinations—where LLMs confidently invent information—are a well-known issue. But even beyond that, how can we trust that the solutions LLMs provide are logically sound?
The truth is, current AI systems don’t engage in true reasoning — but that’s okay because their value lies elsewhere.
Understanding their limitations is key to using them effectively.
Recent studies suggest LLMs do not perform actual logical reasoning.
A recent study from Apple researchers suggests that LLMs are not capable of genuine logical reasoning. Instead, they replicate reasoning steps based on patterns from their training data.
In their paper, “GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models,” Mirzadeh et al. introduce GSM-Symbolic, a benchmark designed to evaluate LLMs’ reasoning abilities. Their findings reveal that LLM performance fluctuates significantly when faced with slight variations in evaluation questions, especially in mathematical reasoning. In other words, LLMs are not reasoning logically.
Whenever they answer our questions, LLMs are simply recognizing and replicating patterns they’ve encountered before.
LLMs are great at pattern matching and searching.
So what do LLMs actually do?
These models are great at pattern recognition and searching. They attempt to replicate the individual reasoning steps found on their training data. In a way, they can replicate the reasoning steps they've seen others follow, but cannot perform genuine logical reasoning on their own. Think of applications like code generation or writing suggestions: here, LLMs recognize patterns based on what you're writing or coding, and suggest changes accordingly.
Fundamentally, this is their greatest limitation.
True logical reasoning is where humans still have an edge — but we must remember to use it.
This is where humans will always have the advantage.
We are beings capable of independent thought. We are all unique and wonderfully made. And while we learn from others around us, we will always have the capability of thinking for ourselves, tapping into creativity and problem-solving in ways machines cannot.
Our ability to think for ourselves, learn from others, and create new solutions is what makes us unique. While AI can mimic reasoning steps, it cannot replace the depth of human creativity and thought. We must never forget this truth.
So what should you take away from this? Embrace your authenticity:
Your unique voice.
Your genuine experiences.
Your humanity.
It's what makes you… you. And that is something to celebrate.
In the end, while LLMs don’t reason as we do, they are powerful tools for specific tasks. The real magic lies in how we use them — thoughtfully and with full awareness of their limits.
