Additional resources

To support your participation in this shared task, we have compiled a list of additional resources that may be useful for understanding the task better, exploring related work, and utilizing domain-specific models.

  • Gasco, L., Fabregat, H., García-Sardiña, L., Estrella, P., Deniz, D., Rodrigo, A., & Zbib, R. (2025, September). Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management. In International Conference of the Cross-Language Evaluation Forum for European Languages (pp. 464-485). Cham: Springer Nature Switzerland.
  • Gasco, L., Fabregat, H., García-Sardiña, L., Estrella, P., Veys, W., Carrino, C. P., De Lange, M., Deniz, D., Rodrigo, A., Decorte, J. J., & Zbib, R. (2026, September). Overview of the TalentCLEF 2026: Skill and Job Title Intelligence for Human Capital Management. In Experimental IR Meets Multilinguality, Multimodality, and Interaction. Lecture Notes in Computer Science, vol. 17087. Cham: Springer Nature Switzerland. Preprint: arXiv:2606.31692
  • Fabregat, H., García-Sardiña, L., Estrella, P., Gasco, L., Carrino, C. P., Deniz, D., Rodrigo, A., & Zbib, R. (2026). Overview of TalentCLEF 2026: Task A – Contextualized Job–Person Matching. In Working Notes of CLEF 2026 – Conference and Labs of the Evaluation Forum.
  • Veys, W., Gasco, L., De Lange, M., & Decorte, J. J. (2026). Overview of TalentCLEF 2026: Task B – Job-Skill Matching with Skill Type Classification. In Working Notes of CLEF 2026 – Conference and Labs of the Evaluation Forum.
  • Gasco, L., Fabregat, H., García-Sardiña, L., Deniz, D., Rodrigo, A., Estrella, P., & Zbib, R. (2025, April). TalentCLEF at CLEF2025: Skill and Job Title Intelligence for Human Capital Management. In European Conference on Information Retrieval (pp. 479-486). Link
  • Zbib, R., Lacasa, L. A., Retyk, F., Poves, R., Aizpuru, J., Fabregat, H., … & García-Casademont, E. (2022). Learning Job Titles Similarity from Noisy Skill Labels. arXiv preprint arXiv:2207.00494
  • Deniz, D., Retyk, F., García-Sardiña, L., Fabregat, H., Gasco, L., & Zbib, R. (2024). Combined Unsupervised and Contrastive Learning for Multilingual Job Recommendation. Link CEUR
  • Decorte, J. J., Van Hautte, J., Demeester, T., & Develder, C. (2021). Jobbert: Understanding job titles through skills. arXiv preprint arXiv:2109.09605
  • Anand, S., Decorte, J. J., & Lowie, N. (2022). Is it required? ranking the skills required for a job-title. arXiv preprint arXiv:2212.08553
  • Zhang, M., Van Der Goot, R., & Plank, B. (2023). ESCOXLM-R: Multilingual taxonomy-driven pre-training for the job market domain. arXiv preprint arXiv:2305.12092
  • Bhola, A., Halder, K., Prasad, A., & Kan, M. Y. (2020, December). Retrieving skills from job descriptions: A language model based extreme multi-label classification framework. In Proceedings of the 28th international conference on computational linguistics (pp. 5832-5842). Link
  • Retyk, F., Gasco, L., Carrino, C. P., Deniz, D., & Zbib, R. (2024). MELO: An Evaluation Benchmark for Multilingual Entity Linking of Occupations. arXiv preprint arXiv:2410.08319.
  • Laosaengpha, N., Tativannarat, T., Rutherford, A., & Chuangsuwanich, E. (2025). Mitigating Language Bias in Cross-Lingual Job Retrieval: A Recruitment Platform Perspective. arXiv preprint arXiv:2502.03220
  • Laosaengpha, N., Tativannarat, T., Piansaddhayanon, C., Rutherford, A., & Chuangsuwanich, E. (2024). Learning Job Title Representation from Job Description Aggregation Network. arXiv preprint arXiv:2406.08055

The per-task overview papers of the 2026 edition and the working notes of the participating teams are listed on the TalentCLEF 2026 Results pages.

Task 1.2 (explainability) and Task 2 (skill ranking under negation) draw on lines of work that are new to TalentCLEF. We will complete this list with the relevant references on LLM-as-a-judge evaluation and explanation faithfulness, on negation cue and scope detection (e.g. NegBERT), and on the JobSkape data-generation framework ahead of the start of each task.

2. External Resources:

  • ESCOXLM-R Model in Huggingface
  • NESTA Taxonomy
  • ESCO Taxonomy
  • WorkRB — TechWolf’s community-driven evaluation framework for AI in the work domain, to which Task 2 contributes. (Link to be added.)
  • Datasets from previous TalentCLEF editions, in the NLP in HR Zenodo community

3. Tutorials:

We will publish a series of notebooks covering the fundamentals, including how to work with the data and upload predictions to Codabench. They will be made available in the talentclef_tutorials repository once the sample set is released.

Last modified September 18, 2026: Uupdate website for 2027 task with results pages (3fde5fe)