arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2407.20906cs.CLcs.AIphysics.data-an
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基于大语言模型的自动化综述生成方法

Automated Review Generation Method Based on Large Language Models

School of Chemical Engineering and Technology(化学工程与技术学院);Key Laboratory for Green Chemical Technology of Ministry of Education(教育部绿色化学技术重点实验室);Collaborative Innovation Center of Chemical Science and Engineering (Tianjin)(化学科学与工程协同创新中心(天津));Joint School of National University of Singapore and Tianjin University(新加坡国立大学与天津大学联合学院);International Campus of Tianjin University(天津大学国际校园);Zhejiang Institute of Tianjin University(天津大学浙江研究院);AIStrucX Technologies(AIStrucX科技)

Shican Wu, Xiao Ma, Dehui Luo, Lulu Li, Xiangcheng Shi, Xin Chang, Xiaoyun Lin, Ran Luo, Chunlei Pei, Changying Du, Zhi-Jian Zhao, Jinlong Gong

AI总结:

提出一种基于大语言模型的自动化综述生成方法,通过多层质量控制有效缓解幻觉问题,在丙烷脱氢催化剂等领域实现高效高质量综述生成,并发布Windows应用提升研究效率。

摘要:

文献研究对科学工作至关重要,但面临信息量激增超出研究人员处理能力的挑战。我们提出了一种基于大语言模型(LLMs)的自动化综述生成方法,以克服效率瓶颈并降低认知负荷。我们经过统计验证的评估框架表明,生成的综述质量与人工质量相当或更高,在各研究领域具有广泛适用性,且无需用户具备领域知识。将该方法应用于丙烷脱氢(PDH)催化剂,快速分析了343篇文章,平均每个LLM账户每篇文章耗时数秒,生成了涵盖35个主题的全面综述;对1041篇文章的扩展分析提供了关于催化剂性质的见解。通过多层质量控制,我们有效缓解了LLMs的幻觉问题,专家验证确认了准确性和引用完整性,同时证明在95%置信度下幻觉风险降至0.5%以下。发布的Windows应用程序支持一键生成综述,提升了研究生产力和文献推荐效率,为更广泛的科学探索奠定了基础。

英文摘要:

Literature research, vital for scientific work, faces the challenge of surging information volumes exceeding researchers' processing capabilities. We present an automated review generation method based on large language models (LLMs) to overcome efficiency bottlenecks and reduce cognitive load. Our statistically validated evaluation framework demonstrates that the generated reviews match or exceed manual quality, offering broad applicability across research fields without requiring users' domain knowledge. Applied to propane dehydrogenation (PDH) catalysts, our method swiftly analyzed 343 articles, averaging seconds per article per LLM account, producing comprehensive reviews spanning 35 topics, with extended analysis of 1041 articles providing insights into catalysts' properties. Through multi-layered quality control, we effectively mitigated LLMs' hallucinations, with expert verification confirming accuracy and citation integrity while demonstrating hallucination risks reduced to below 0.5\% with 95\% confidence. Released Windows application enables one-click review generation, enhancing research productivity and literature recommendation efficiency while setting the stage for broader scientific explorations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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