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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
    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
    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
    Talat, Z., Névéol, A., Biderman, S., Clinciu, M., Dey, M., Longpre, S., Luccioni, A. S., Masoud, M., Mitchell, M., Radev, D., Sharma, S., Subramonian, A., Tae, J., Tan, S., Tunuguntla, D., & van der Wal, O. (2022). You Reap What You Sow: On the Challenges of Bias Evaluation Under Multilingual Settings. In A. Fan, S. Ilic, T. Wolf, & M. Gallé (Eds.), Challenges & Perspectives in Creating Large Language Models: 2022 : Proceedings of the Workshop : May 27, 2022 (pp. 26-41). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.bigscience-1.3
  • 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
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