Current LLMs are not capable of genuine logical reasoning; instead, they attempt to replicate the reasoning steps observed in their training data. This implies their outputs can be brittle and unreliable.
The fragility highlighted in these new results reinforces previous research that suggests LLMs' probabilistic pattern matching lacks the formal understanding required for truly reliable mathematical reasoning capabilities.
#artificial-intelligence #large-language-models #mathematical-reasoning #research-study #limitations
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