用大模型+模糊决策，自动评估程序员简历并精准排序。
When LLM meets Fuzzy-TOPSIS for Personnel Selection through Automated Profile Analysis
- 结合大模型与模糊TOPSIS，处理评价中的主观模糊性。
- 对经验与整体评分准确率达91%，接近人工评估水平。
- 适合想提升招聘效率、减少偏见的科技企业使用。
在竞争激烈的就业环境中，人员选拔对组织成功至关重要。本研究提出一种自动化人员选拔系统，利用先进的自然语言处理（NLP）方法评估并排序软件工程候选人。通过整合包含教育背景、工作经验、技能和自我介绍等特征的领英（LinkedIn）档案，并引入专家评估作为标准，构建了独特数据集。研究将大语言模型（LLMs）与多准则决策（MCDM）理论结合，提出LLM-TOPSIS框架。在此框架中，采用模糊逻辑增强的TOPSIS方法（Fuzzy TOPSIS），以应对人类评估中的固有模糊性与主观性。使用三角模糊数（TFNs）描述准则权重与评分，有效应对候选人评价中常见的不确定性。候选者排名采用微调后的DistilRoBERTa模型，结合模糊TOPSIS方法，其排名结果与人工专家评估高度一致，对经验属性和总体属性的准确率分别达到91%。研究强调了基于NLP的框架在提升招聘流程的可扩展性、一致性及降低偏见方面的潜力。未来工作将聚焦于扩充数据集、提升模型可解释性，并在真实招聘场景中验证系统实用性。本研究展示了将NLP与模糊决策方法融合在人员选拔中的巨大潜力，为招聘难题提供可扩展且公平的解决方案。
原文摘要 · Abstract (English)
In this highly competitive employment environment, the selection of suitable personnel is essential for organizational success. This study presents an automated personnel selection system that utilizes sophisticated natural language processing (NLP) methods to assess and rank software engineering applicants. A distinctive dataset was created by aggregating LinkedIn profiles that include essential features such as education, work experience, abilities, and self-introduction, further enhanced with expert assessments to function as standards. The research combines large language models (LLMs) with multicriteria decision-making (MCDM) theory to develop the LLM-TOPSIS framework. In this context, we utilized the TOPSIS method enhanced by fuzzy logic (Fuzzy TOPSIS) to address the intrinsic ambiguity and subjectivity in human assessments. We utilized triangular fuzzy numbers (TFNs) to describe criteria weights and scores, thereby addressing the ambiguity frequently encountered in candidate evaluations. For candidate ranking, the DistilRoBERTa model was fine-tuned and integrated with the fuzzy TOPSIS method, achieving rankings closely aligned with human expert evaluations and attaining an accuracy of up to 91% for the Experience attribute and the Overall attribute. The study underlines the potential of NLP-driven frameworks to improve recruitment procedures by boosting scalability, consistency, and minimizing prejudice. Future endeavors will concentrate on augmenting the dataset, enhancing model interpretability, and verifying the system in actual recruitment scenarios to better evaluate its practical applicability. This research highlights the intriguing potential of merging NLP with fuzzy decision-making methods in personnel selection, enabling scalable and unbiased solutions to recruitment difficulties.
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