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English(EN) Learning Mechanistic Reasoning for Chemical Reactions with Large Language Models

LLM通过新数据集和基准测试增强化学推理能力

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新方法，通过关注反应机制来提高大型语言模型（LLM）的化学推理能力。他们创建了一个大规模数据集，并引入了FukuyamaBench，这是一个旨在测试分层机制推理的基准。他们微调的Qwen3-30B-A3B模型在FukuyamaBench上实现了8.3%的精确路径匹配，优于得分为5.1%的专用FlowER模型。这表明使用面向机制的数据训练LLM可以显著增强其化学推理能力。 AI

影响 增强了LLM在化学等专业科学领域的应用能力，可能提高了AI在研发中的效用。

排序理由 该集群包含一篇详细介绍LLM化学推理新方法和基准的学术论文。

在 arXiv cs.CL 阅读 →

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LLM通过新数据集和基准测试增强化学推理能力

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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Xingyu Dang, Haocheng Tang, Junmei Wang, Yanjun Li ·

    使用大型语言模型学习化学反应的机制推理

    arXiv:2607.12771v1 Announce Type: cross Abstract: Reaction mechanisms consist of the step-by-step sequences of elementary reactions that explain chemical transformations. Learning the mechanism logic is therefore essential for enhancing the fundamental chemical intelligence of la…

  2. arXiv cs.LG TIER_1 English(EN) · Yanjun Li ·

    使用大型语言模型学习化学反应的机械推理

    Reaction mechanisms consist of the step-by-step sequences of elementary reactions that explain chemical transformations. Learning the mechanism logic is therefore essential for enhancing the fundamental chemical intelligence of large language models (LLMs). The stepwise deduction…

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