A recent study reveals that large language models like GPT-3 struggle with complex reasoning tasks, despite near-perfect accuracy on simpler, in-domain examples. The research shows that Transformers reduce multi-step reasoning to pattern matching, leading to errors in chaining multiple steps. The findings highlight the need for models that can systematically extrapolate beyond their training data for complex tasks.
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