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Results: 11
Number of items: 11
  • Open Access
    van der Wal, O. D. (2026). Taking a step back: Measuring and mitigating bias in language models. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    van der Wal, O., Bachmann, D., Leidinger, A., van Maanen, L., Zuidema, W., & Schulz, K. (2024). Undesirable Biases in NLP: Addressing Challenges of Measurement. Journal of Artificial Intelligence Research, 79, 1-40. https://doi.org/10.1613/jair.1.15195
  • Open Access
    Bachmann, D., van der Wal, O., Chvojka, E., Zuidema, W. H., van Maanen, L., & Schulz, K. (2024). fl-IRT-ing with Psychometrics to Improve NLP Bias Measurement. Minds and Machines, 34(4), Article 37. https://doi.org/10.1007/s11023-024-09695-9
  • Open Access
    Biderman, S., Schoelkopf, H., Anthony, Q., Bradley, H., O'Brien, K., Hallahan, E., Khan, M. A., Purohit, S., Sai Prashanth, U. S., Raff, E., Skowron, A., Sutawika, L., & van der Wal, O. (2023). Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling. Proceedings of Machine Learning Research, 202, 2397-2430. https://proceedings.mlr.press/v202/biderman23a.html
  • Open Access
    Chintam, A., Beloch, R., Zuidema, W., Hanna, M., & van der Wal, O. (2023). Identifying and Adapting Transformer-Components Responsible for Gender Bias in an English Language Model. In Y. Belinkov, S. Hao, J. Jumelet, N. Kim, A. McCarthy, & H. Mohebbi (Eds.), BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP: Proceedings of the Sixth Workshop : EMNLP 2023 : December 7, 2023 (pp. 379-394). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.blackboxnlp-1.29
  • Open Access
    Jumelet, J., Hanna, M., de Heer Kloots, M., Langedijk, A., Pouw, C., & van der Wal, O. (2023). ChapGTP, ILLC’s Attempt at Raising a BabyLM: Improving Data Efficiency by Automatic Task Formation. In A. Warstadt, A. Mueller, L. Choshen, E. Wilcox, C. Zhuang, J. Ciro, R. Mosquera, B. Paranjabe, A. Williams, T. Linzen, & R. Cotterell (Eds.), Findings of the BabyLM Challenge: Sample-efficient pretraining on developmentally plausible corpora (pp. 74-85). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.conll-babylm.6
  • Open Access
    Sarti, G., Feldhus, N., Sickert, L., & van der Wal, O. (2023). Inseq: An interpretability toolkit for sequence generation models. In D. Bollegala, R. Hang, & A. Ritter (Eds.), The 61st Conference of the Association for Computational Linguistics: System Demonstrations: ACL-DEMO 2023 : Proceedings of the System Demonstrations : July 10-12, 2023 (pp. 421-435). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-demo.40
  • Open Access
    BigScience Workshop, Le Scao, T., Kalo, J.-C., van der Wal, O., & Wang, B. (2023). BLOOM: A 176B-Parameter Open-Access Multilingual Language Model. (v4 ed.) ArXiv. https://doi.org/10.48550/arXiv.2211.05100
  • Open Access
    van der Wal, O., Jumelet, J., Schulz, K., & Zuidema, W. (2022). The Birth of Bias: A case study on the evolution of gender bias in an English language model. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2207.10245
  • Open Access
    van der Wal, O., Bachmann, D., Leidinger, A., van Maanen, L., Zuidema, W., & Schulz, K. (2022). Undesirable biases in NLP: Averting a crisis of measurement. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2211.13709
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