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Peking University
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Efficiency in LLM-generated data science code can be drastically improved, even when correctness appears high, revealing hidden inefficiencies that traditional benchmarks overlook.
Even when LLMs translate code correctly, over 20% of the time it's surprisingly inefficient due to algorithmic flaws, poor language choices, or resource mismanagement.
