Manon Reusens

Manon Reusens

Research Manager ACRAI at the University of Antwerp- Guest Lecturer at the KU Leuven

Publications

You can also find my articles on my Google Scholar profile.

🧑‍💼 Are Economists Always More Introverted? Analyzing Consistency in Persona-Assigned LLMs

Published in Findings of the Association for Computational Linguistics: EMNLP 2025, 2025

We introduce a novel framework to analyze consistency in persona-assigned LLMs.

Recommended citation: Manon Reusens, Bart Baesens, and David Jurgens. 2025. Are Economists Always More Introverted? Analyzing Consistency in Persona-Assigned LLMs. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 11268–11287, Suzhou, China. Association for Computational Linguistics.
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🏠 On the Performance of LLMs for Real Estate Appraisal

Published in ECML PKDD 2025, 2025

We systematically evaluate leading LLMs on diverse international housing datasets, comparing zero-shot, few-shot, market report-enhanced, and hybrid pro-mpting techniques.

Recommended citation: Geerts, M., Reusens, M., Baesens, B., vanden Broucke, S., De Weerdt, J. (2026). On the Performance of LLMs for Real Estate Appraisal. In: Dutra, I., et al. Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. ECML PKDD 2025. Lecture Notes in Computer Science(), vol 16021. Springer, Cham. https://doi.org/10.1007/978-3-032-06118-8_12
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🤖 LLM anthropomorphization: balancing ethics and business value

Published in Journal of Business Analytics, 2025

This viewpoint article offers a cross-disciplinary synthesis of insights from Computer Science, Business, Psychology, and Law to develop practical, actionable guidelines for responsible LLM deployment.

Recommended citation: Reusens, M., & Baesens, B. (2025). LLM anthropomorphization: balancing ethics and business value. Journal of Business Analytics, 1–18. https://doi.org/10.1080/2573234X.2025.2551951
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⚖️ Evaluating text classification: A benchmark study

Published in Expert Systems with Applications, 2024

This paper includes a benchmark study comparing different techniques for text classification.

Recommended citation: Reusens, M., Stevens, A., Tonglet, J., De Smedt, J., Verbeke, W., Vanden Broucke, S., & Baesens, B. (2024). Evaluating text classification: A benchmark study. Expert Systems with Applications, 124302.
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✅ SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQA

Published in EMNLP, 2023

This paper introduces a new exemplar selection method named SEER for in-context hybridQA.

Recommended citation: Tonglet, J., Reusens, M., Borchert, P., & Baesens, B. (2023, December). SEER: A Knapsack approach to Exemplar Selection for In-Context HybridQA. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 13569-13583).
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🌍 Investigating Bias in Multilingual Language Models: Cross-Lingual Transfer of Debiasing Techniques

Published in EMNLP, 2023

This paper investigates the transferability of debiasing techniques across different languages within multilingual models.

Recommended citation: Reusens, M., Borchert, P., Mieskes, M., De Weerdt, J., & Baesens, B. (2023, December). Investigating Bias in Multilingual Language Models: Cross-Lingual Transfer of Debiasing Techniques. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 2887-2896).
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📘 Topic modelling applied on innovation studies of Flemish companies

Published in Journal of Business Analytics, 2023

This paper uses topic modelling to analyze innovation within Flemish companies based on their websites.

Recommended citation: Crijns, A., Vanhullebusch, V., Reusens, M., Reusens, M., & Baesens, B. (2023). Topic modelling applied on innovation studies of Flemish companies. Journal of Business Analytics, 6(4), 243-254.
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💡 Benchmarking conventional outlier detection methods

Published in International Conference on Research Challenges in Information Science, 2022

This paper benchmarks several outlier detection methods.

Recommended citation: Tiukhova, E., Reusens, M., Baesens, B., & Snoeck, M. (2022, May). Benchmarking conventional outlier detection methods. In International Conference on Research Challenges in Information Science (pp. 597-613). Cham: Springer International Publishing.
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