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| Format: | Preprint |
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2026
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| Online-Zugang: | https://arxiv.org/abs/2604.13055 |
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| author | De Lange, Matthias Veys, Warre Retyk, Federico Deniz, Daniel Jouanneau, Warren Zhang, Mike Bielinski, Aleksander Jouffroy, Emma Clobes, Nicole Baranowska, Nina Graus, David Palyart, Marc Zbib, Rabih Gkatzia, Dimitra Demeester, Thomas De Bie, Tijl Bogers, Toine Decorte, Jens-Joris Van Hautte, Jeroen |
| author_facet | De Lange, Matthias Veys, Warre Retyk, Federico Deniz, Daniel Jouanneau, Warren Zhang, Mike Bielinski, Aleksander Jouffroy, Emma Clobes, Nicole Baranowska, Nina Graus, David Palyart, Marc Zbib, Rabih Gkatzia, Dimitra Demeester, Thomas De Bie, Tijl Bogers, Toine Decorte, Jens-Joris Van Hautte, Jeroen |
| contents | Today's evolving labor markets rely increasingly on recommender systems for hiring, talent management, and workforce analytics, with natural language processing (NLP) capabilities at the core. Yet, research in this area remains highly fragmented. Studies employ divergent ontologies (ESCO, O*NET, national taxonomies), heterogeneous task formulations, and diverse model families, making cross-study comparison and reproducibility exceedingly difficult. General-purpose benchmarks lack coverage of work-specific tasks, and the inherent sensitivity of employment data further limits open evaluation. We present \textbf{WorkRB} (Work Research Benchmark), the first open-source, community-driven benchmark tailored to work-domain AI. WorkRB organizes 13 diverse tasks from 7 task groups as unified recommendation and NLP tasks, including job/skill recommendation, candidate recommendation, similar item recommendation, and skill extraction and normalization. WorkRB enables both monolingual and cross-lingual evaluation settings through dynamic loading of multilingual ontologies. Developed within a multi-stakeholder ecosystem of academia, industry, and public institutions, WorkRB has a modular design for seamless contributions and enables integration of proprietary tasks without disclosing sensitive data. WorkRB is available under the Apache 2.0 license at https://github.com/techwolf-ai/WorkRB. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13055 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | WorkRB: A Community-Driven Evaluation Framework for AI in the Work Domain De Lange, Matthias Veys, Warre Retyk, Federico Deniz, Daniel Jouanneau, Warren Zhang, Mike Bielinski, Aleksander Jouffroy, Emma Clobes, Nicole Baranowska, Nina Graus, David Palyart, Marc Zbib, Rabih Gkatzia, Dimitra Demeester, Thomas De Bie, Tijl Bogers, Toine Decorte, Jens-Joris Van Hautte, Jeroen Computation and Language Artificial Intelligence Today's evolving labor markets rely increasingly on recommender systems for hiring, talent management, and workforce analytics, with natural language processing (NLP) capabilities at the core. Yet, research in this area remains highly fragmented. Studies employ divergent ontologies (ESCO, O*NET, national taxonomies), heterogeneous task formulations, and diverse model families, making cross-study comparison and reproducibility exceedingly difficult. General-purpose benchmarks lack coverage of work-specific tasks, and the inherent sensitivity of employment data further limits open evaluation. We present \textbf{WorkRB} (Work Research Benchmark), the first open-source, community-driven benchmark tailored to work-domain AI. WorkRB organizes 13 diverse tasks from 7 task groups as unified recommendation and NLP tasks, including job/skill recommendation, candidate recommendation, similar item recommendation, and skill extraction and normalization. WorkRB enables both monolingual and cross-lingual evaluation settings through dynamic loading of multilingual ontologies. Developed within a multi-stakeholder ecosystem of academia, industry, and public institutions, WorkRB has a modular design for seamless contributions and enables integration of proprietary tasks without disclosing sensitive data. WorkRB is available under the Apache 2.0 license at https://github.com/techwolf-ai/WorkRB. |
| title | WorkRB: A Community-Driven Evaluation Framework for AI in the Work Domain |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2604.13055 |