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作 者:陈昱成 韩涛[1,2] Chen Yucheng;Han Tao(National Science Library,Chinese Academy of Sciences,Beijing 100190;Department of Information Resourses Management,School of Economics and Management,University of Chinese Academy of Sciences,Beijing 100190)
机构地区:[1]中国科学院文献情报中心,北京100190 [2]中国科学院大学经济与管理学院信息资源管理系,北京100190
出 处:《情报杂志》2024年第10期144-149,143,共7页Journal of Intelligence
基 金:国家社会科学基金项目“支撑AI4Science的科技图书馆知识服务内容研究”(编号:22BTQ019);中国科学院文献情报能力建设专项项目“‘智慧数据+AI’支撑科学创新实验方法的推理发现研究”(编号:E329090905)研究成果。
摘 要:[研究目的]科研人员从海量的文献中检索与自己研究方向相关的文献需要耗费大量的时间,自动提取研究问题与研究方法句对于学者在研究过程中评估和选择合适的方法具有重要意义。[研究方法]基于多种深度学习分类器构建物理领域研究方向的分类器,筛选高能物理领域的研究,对高能物理领域的论文利用生成式大模型进行研究问题与研究方法句的生成,微调baichuan-7B构建高能物理领域的问答推理工具。[研究结论]研究表明,在研究领域分类中,SciBERT分类结果较好,F1值为82.71%,baichuan-13B在研究问题与研究方法句子生成研究中,2-gram的BLEU值为0.311。最终通过研究问题与研究方法库微调大模型,可实现高能物理领域的问答功能。[Research purpose]It takes a lot of time for researchers to search for literature related to their research direction from a massive amount of literatures.Automatically extracting research questions and method sentences is of great significance for scholars to evaluate and choose appropriate methods during the research process.[Research method]Based on various deep learning classifiers,we construct classifiers for research directions in the field of physics,screen research in the field of high-energy physics,use generative large models to generate research questions and research method sentences for papers in the field of high-energy physics,and fine tune baichuan-7B to construct a question answering reasoning tool in the field of high-energy physics.[Research conclusion]Research has shown that SciBERT performs well in research field classification,with an F1 value of 82.71%.Baichuan-13B has a BLEU value of 0.311 for 2-gram in research question and research method sentence generation.Ultimately,by fine-tuning the large model through the research question and research method library,the question answering function in the field of high-energy physics can be achieved.
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