对比三类方法在职位匹配与技能预测中的表现，发现提示方法最优。
NLPnorth @ TalentCLEF 2025: Comparing Discriminative, Contrastive, and Prompt-Based Methods for Job Title and Skill Matching
- 采用提示、分类和对比三种方法进行多语言职位匹配与技能预测。
- 提示法在职位匹配任务中达0.492 MAP，分类法在技能预测中达0.290 MAP。
- 利用ESCO额外数据提升性能，大模型表现最佳，适合多语言求职系统研究者。
职位标题匹配在计算招聘市场领域具有重要意义，可提升自动候选人匹配、职业路径预测和就业市场分析。将职位标题与技能对齐可视为该任务的延伸，同样具备应用价值。本文介绍了NLPnorth团队在TalentCLEF 2025中的两项任务：多语言职位标题匹配（Task A）与基于职位标题的技能预测（Task B）。我们比较了微调后的分类、对比和提示方法。在任务A中，提示方法在英语、西班牙语和德语测试集上平均取得0.492的MAP；任务B中，微调分类方法获得0.290的MAP。我们还通过ESCOP拉取各职位与技能的语言特定标题及描述作为额外数据。总体而言，大型多语言模型表现最佳。根据初步结果，任务A排名5/20，任务B排名3/14。
原文摘要 · Abstract (English)
Matching job titles is a highly relevant task in the computational job market domain, as it improves e.g., automatic candidate matching, career path prediction, and job market analysis. Furthermore, aligning job titles to job skills can be considered an extension to this task, with similar relevance for the same downstream tasks. In this report, we outline NLPnorth's submission to TalentCLEF 2025, which includes both of these tasks: Multilingual Job Title Matching, and Job Title-Based Skill Prediction. For both tasks we compare (fine-tuned) classification-based, (fine-tuned) contrastive-based, and prompting methods. We observe that for Task A, our prompting approach performs best with an average of 0.492 mean average precision (MAP) on test data, averaged over English, Spanish, and German. For Task B, we obtain an MAP of 0.290 on test data with our fine-tuned classification-based approach. Additionally, we made use of extra data by pulling all the language-specific titles and corresponding \emph{descriptions} from ESCO for each job and skill. Overall, we find that the largest multilingual language models perform best for both tasks. Per the provisional results and only counting the unique teams, the ranking on Task A is 5$^{\text{th}}$/20 and for Task B 3$^{\text{rd}}$/14.
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