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LLMs generate ‘fluent nonsense’ when reasoning outside their training zone
Chain-of-Thought isn't a plug-and-play solution. For developers, this research offers a blueprint for LLM testing and strategic fine-tuning.
A new study from Arizona State University researchers suggests that the celebrated “Chain-of-Thought” (CoT) reasoning in Large Language Models (LLMs) may be more of a “brittle mirage” than genuine intelligence. As the paper notes, “theoretical and empirical evidence shows that CoT generalizes well only when test inputs share latent structures with training data; otherwise, performance declines sharply.” The researchers offer a direct warning to practitioners, highlighting “the risk of relying on CoT as a plug-and-play solution for reasoning tasks and caution against equating CoT-style output with human thinking.” They provide three key pieces of advice for developers building applications with LLMs.
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