CV

Education

• B.S. in Liberal Arts and Sciences, Amsterdam University College, 2021
• M.S. in Artificial Intelligence, University of Amsterdam, 2023
• Ph.D in Safety in Conversational AI, University of Amsterdam, 2027 (expected)

Work experience

• Nov 2022 - May 2023: Teaching Assistent
  • University of Amsterdam
  • 3 first-year courses in the MSc AI program: Natural Language Processing 1, Knowledge Representation and Reasoning, and Automated Planning.
  • Duties included: Teaching tutorial sessions, assisting students with their assignments, and answering their questions. Grading of assignments and exams.
• Jan 2020 - Jun 2021: Data scientist Intern
  • Machine Learning Programs
  • Duties included: Writing literate programming documents analyzing data sets, and model performance. Incorporating domain knowledge on car insurance. Training machine learning models for premium prediction and propensity to claim. Supervising groups of undergraduates during their one month internship projects.

Publications

Actionable Interpretability for Churn Classification: A Text Bottleneck Model Case Study at a Major Telecom Provider

Adrian Sauter, **Vera Neplenbroek**, Georgios Vlassopoulos, and Gianluigi Bardelloni. 2026. Actionable Interpretability for Churn Classification: A Text Bottleneck Model Case Study at a Major Telecom Provider. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track), pages 1005–1024, San Diego, California, USA. Association for Computational Linguistics.

Topics as Proxies for Sociodemographics: How Conversational Context Affects LLM Answers

Neplenbroek, V., Sarti, G., Bisazza, A., & Fernández, R. (2026). Topics as Proxies for Sociodemographics: How Conversational Context Affects LLM Answers. arXiv preprint arXiv:2606.02776.

One Persona, Many Cues, Different Results: How Sociodemographic Cues Impact LLM Personalization

Franziska Weeber, Vera Neplenbroek, Jan Batzner, and Sebastian Padó. 2026. One Persona, Many Cues, Different Results: How Sociodemographic Cues Impact LLM Personalization. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 44892–44921, San Diego, California, United States. Association for Computational Linguistics.

Reading Between the Prompts: How Stereotypes Shape LLM’s Implicit Personalization

Vera Neplenbroek, Arianna Bisazza, and Raquel Fernández. 2025. Reading Between the Prompts: How Stereotypes Shape LLM’s Implicit Personalization. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 20378–20411, Suzhou, China. Association for Computational Linguistics.

Cross-Lingual Transfer of Debiasing and Detoxification in Multilingual LLMs: An Extensive Investigation

Vera Neplenbroek, Arianna Bisazza, and Raquel Fernández. 2025. Cross-Lingual Transfer of Debiasing and Detoxification in Multilingual LLMs: An Extensive Investigation. In Findings of the Association for Computational Linguistics: ACL 2025, pages 2805–2830, Vienna, Austria. Association for Computational Linguistics.

MBBQ: A Dataset for Cross-Lingual Comparison of Stereotypes in Generative LLMs

Neplenbroek, V., Bisazza, A. and Fernández, R., 2024. MBBQ: A Dataset for Cross-Lingual Comparison of Stereotypes in Generative LLMs. In the first Conference on Language Modeling (COLM) 2024.

LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks

Anna Bavaresco, Raffaella Bernardi, Leonardo Bertolazzi, Desmond Elliott, Raquel Fernández, Albert Gatt, Esam Ghaleb, Mario Giulianelli, Michael Hanna, Alexander Koller, Andre Martins, Philipp Mondorf, Vera Neplenbroek, Sandro Pezzelle, Barbara Plank, David Schlangen, Alessandro Suglia, Aditya K Surikuchi, Ece Takmaz, and Alberto Testoni. 2025. LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 238–255, Vienna, Austria. Association for Computational Linguistics.

[Re] Replication study of ‘Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling’

Neplenbroek, V., Perdijk, S., and Prins, V. 2022. [Re] Replication study of ’Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling.’ ReScience C 8, 2, #29.