基于自然语言处理的专家分配系统用于玛丽·斯克洛多夫斯卡-库里行动
Elena Álvarez-García1, Daniel García-Costa1, Ilse De Waele2
1Department of Computer Science, University of Valencia, Burjassot, Spain.
Scientific reports
|January 26, 2026
概括
我们使用大型语言模型 (LLM) 开发了一种新的专家分配系统,以改善研究评估. 该系统利用语义相似性,在准确地将专家分配给项目提案方面明显优于传统的基于关键字的方法.
科学领域:
- 研究评估研究评估
- 科学中的人工智能.
背景情况:
- 传统的专家分配依赖于关键字匹配,通常需要副主席 (VC) 进行手动校正.
- 像单一评估平台 (SEP) 这样的现有系统在语义相关性和准确性方面存在局限性.
研究的目的:
- 开发和评估使用自然语言处理 (NLP) 和大型语言模型 (LLM) 的新型专家分配系统.
- 为了提高专家建议匹配的语义相关性,超出了简单的关键字相关性.
主要方法:
- 专家出版物的综合动态检索通过ORCID与GALACTICA,一个科学LLM.
- 在提案摘要和专家出版物之间计算精细的语义相似性.
- 评估了三个相似性聚合策略:总和,产品和最大值.
主要成果:
- 最大相似度聚合策略实现了0.82的AUC,超过了传统的SEP系统 (AUC = 0.75).
- 基于LLM的系统显著超过了总和 (AUC = 0.69) 和产品 (AUC = 0.57) 策略.
- "最大限度"方法密切地复制了VC审查的任务,表明有效地捕捉了人类的决策.
结论:
- 基于LLM的语义匹配为专家分配提供了比传统信息检索 (IR) 系统更准确,更可扩展的替代方案.
- 最大相似性策略有效地确定了最相关的专家匹配,反映了人类的判断.
- 这种方法为更好的专家差异化提供了细微的,细粒度的评级,超过了离散的亲和度评分.
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