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.

翻译：在竞争激烈的就业环境中，选拔合适的人员对组织成功至关重要。本研究提出了一种自动化人员选拔系统，利用先进的自然语言处理（NLP）方法评估和排序软件工程职位申请者。通过整合包含教育背景、工作经验、技能与自我介绍等关键特征的LinkedIn个人资料，并辅以专家评估作为基准，构建了一个独特的数据集。该研究将大型语言模型（LLMs）与多准则决策（MCDM）理论相结合，开发了LLM-TOPSIS框架。在此框架中，我们采用经模糊逻辑增强的TOPSIS方法（模糊TOPSIS）来处理人类评估中固有的模糊性与主观性。通过引入三角模糊数（TFNs）来描述准则权重与评分，从而有效应对候选人评估中常见的模糊性问题。在候选人排序环节，我们对DistilRoBERTa模型进行微调，并将其与模糊TOPSIS方法集成，最终获得的排序结果与人类专家评估高度一致，其中"经验"属性与"综合"属性的准确率最高可达91%。本研究凸显了NLP驱动框架在提升招聘流程可扩展性、一致性并减少偏见方面的潜力。未来工作将集中于扩展数据集、增强模型可解释性，并在实际招聘场景中验证系统，以进一步评估其实际适用性。本研究表明，将NLP与模糊决策方法相结合应用于人员选拔领域具有广阔前景，能够为招聘难题提供可扩展且无偏见的解决方案。