English(EN) Can Decision Trees Teach Large Language Models? Distilling Verbalized Knowledge for Molecular Property Prediction

决策树增强大型语言模型在分子性质预测方面的能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-28 04:00

研究人员开发了一种名为TreeKD的新方法，以提高大型语言模型（LLMs）在分子性质预测任务中的准确性，这项任务在药物发现中至关重要。TreeKD通过将从基于分子特征训练的专业决策树中提取的知识，通过口头提示蒸馏到LLMs中。这种方法增强了LLMs的内部知识和预测能力。该方法还采用了一种称为规则一致性的技术，用于在测试时聚合预测，进一步提高性能。 AI

影响这项研究通过提高LLMs在预测分子性质方面的准确性，可能显著推动LLMs在药物发现中的应用。

排序理由该集群包含一篇研究论文，详细介绍了一种改进LLM在特定任务上性能的新颖方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Khiem Le, Sreejata Dey, Marcos Mart\'inez Galindo, Vanessa Lopez, Ting Hua, Nitesh V. Chawla, Hoang Thanh Lam · 2026-05-28 04:00

决策树能否教会大型语言模型？提炼口头知识用于分子性质预测

arXiv:2603.12344v2 Announce Type: replace Abstract: Molecular Property Prediction (MPP) is a fundamental problem in drug discovery that has recently attracted growing attention. Large Language Models (LLMs), known for their impressive proficiency across domains, show promise as g…

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决策树能否教会大型语言模型？提炼口头知识用于分子性质预测

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