On the TalentCLEF 2025 benchmark, zero-shot prompting gave the best multilingual job-title matching result, while fine-tuned classification gave the best job-skill prediction result.
Natural Language Processing for Human Resources: A Survey
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abstract
Advances in Natural Language Processing (NLP) have the potential to transform HR processes, from recruitment to employee management. While recent breakthroughs in NLP have generated significant interest in its industrial applications, a comprehensive overview of how NLP can be applied across HR activities is still lacking. This paper discovers opportunities for researchers and practitioners to harness NLP's transformative potential in this domain. We analyze key fundamental tasks such as information extraction and text classification, and their roles in downstream applications like recommendation and language generation, while also discussing ethical concerns. Additionally, we identify gaps in current research and encourage future work to explore holistic approaches for achieving broader objectives in this field.
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NLPnorth @ TalentCLEF 2025: Comparing Discriminative, Contrastive, and Prompt-Based Methods for Job Title and Skill Matching
On the TalentCLEF 2025 benchmark, zero-shot prompting gave the best multilingual job-title matching result, while fine-tuned classification gave the best job-skill prediction result.