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Stronger reasoning in LLMs using Code Interpreters hinges on the strategic use of crucial tokens and cognitive behaviors, revealing both potential and constraints in model performance.
LLMs may excel at mimicking majority human opinions, but they struggle to reproduce valid minority interpretations, revealing a critical flaw in using human consensus as the gold standard for LLM evaluation.
