LLM生成的装箱算法看似新颖，实则可被更简单高效的算法替代。
An In-depth Study of LLM Contributions to the Bin Packing Problem
- 用人类可读的规则生成装箱策略，但对专家仍不透明。
- 新算法在均匀与威布尔分布下效率更高、解释性更强。
- 适合关注LLM科学价值评估与算法可解释性的研究者。
近期研究表明，大型语言模型（LLMs）可能为数学发现提供新思路。这一观点源于报告称基于LLM的遗传算法在均匀分布和威布尔分布下的在线装箱问题中生成了具有新见解的启发式算法。本文通过深入分析这些由LLM生成的启发式策略，考察其行为与可解释性。尽管规则形式为人可读，但对领域专家仍高度模糊。基于此分析，我们提出一类专为此类装箱实例设计的新算法。所推导出的算法显著更简单、高效、可解释且更具泛化能力，表明所考虑实例本身相对简单。我们进一步讨论该主张的局限性，认为其建立在错误假设之上——即这些实例此前未被研究。我们的发现强调，在评估LLM生成输出的科学价值时，必须进行严格验证与情境化分析。
原文摘要 · Abstract (English)
Recent studies have suggested that Large Language Models (LLMs) could provide interesting ideas contributing to mathematical discovery. This claim was motivated by reports that LLM-based genetic algorithms produced heuristics offering new insights into the online bin packing problem under uniform and Weibull distributions. In this work, we reassess this claim through a detailed analysis of the heuristics produced by LLMs, examining both their behavior and interpretability. Despite being human-readable, these heuristics remain largely opaque even to domain experts. Building on this analysis, we propose a new class of algorithms tailored to these specific bin packing instances. The derived algorithms are significantly simpler, more efficient, more interpretable, and more generalizable, suggesting that the considered instances are themselves relatively simple. We then discuss the limitations of the claim regarding LLMs' contribution to this problem, which appears to rest on the mistaken assumption that the instances had previously been studied. Our findings instead emphasize the need for rigorous validation and contextualization when assessing the scientific value of LLM-generated outputs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。