How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmark
<|reference_start|>How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmark: The emergence of large language models (LLMs) has significantly pushed the frontiers of program synthesis. Advancement of LLM-based program synthesis calls for a thorough evaluation of LLM-generated code. Most evaluation frameworks focus on the (functional) correctness of generated code; efficiency, as an important measure of code quality, has been overlooked in existing evaluations. In this work, we develop ENAMEL (EfficeNcy AutoMatic EvaLuator), a rigorous and high-standard benchmark for evaluating the capability of LLMs in generating efficient code. Firstly, we propose a new efficiency metric called eff@k, which generalizes the pass@k metric from correctness to efficiency and appropriately handles right-censored execution time. Furthermore, we derive an unbiased and variance-reduced estimator of eff@k via Rao--Blackwellization; we also provide a numerically stable implementation for the new estimator. Secondly, to set a high-standard for efficiency evaluation, we employ a human expert to design best algorithms and implementations as our reference solutions of efficiency, many of which are much more efficient than existing canonical solutions in HumanEval and HumanEval+. Moreover, to ensure a rigorous evaluation, we employ a human expert to curate strong test case generators to filter out wrong code and differentiate suboptimal algorithms. An extensive study across 30 popular LLMs using our benchmark ENAMEL shows that LLMs still fall short of generating expert-level efficient code. Using two subsets of our problem set, we demonstrate that such deficiency is because current LLMs struggle in designing advanced algorithms and are barely aware of implementation optimization. Our benchmark is publicly available at https://github.com/q-rz/enamel .<|reference_end|>
@article{qiu2024how,
    title={How Efficient is LLM-Generated Code? A Rigorous & High-Standard
  Benchmark},
    author={Ruizhong Qiu, Weiliang Will Zeng, Hanghang Tong, James Ezick,
  Christopher Lott},
    journal={arXiv preprint arXiv:2406.06647},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06647},
    primaryClass={cs.SE cs.AI cs.LG}
}