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Results: 108
Number of items: 108
  • 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
    de Heer Kloots, M., & Zuidema, W. (2024). Human-like Linguistic Biases in Neural Speech Models: Phonetic Categorization and Phonotactic Constraints in Wav2Vec2.0. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 25, 4593-4597. https://doi.org/10.21437/Interspeech.2024-2490
  • 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
    Cornelissen, B. J. M. (2024). Measuring musics: Notes on modes, motifs, and melodies. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Langedijk, A., Mohebbi, H., Sarti, G., Zuidema, W., & Jumelet, J. (2024). DecoderLens: Layerwise Interpretation of Encoder-Decoder Transformers. In K. Duh, H. Gomez, & S. Bethard (Eds.), Findings of the Association for Computational Linguistics: NAACL 2024: Findings: Findings 2024 : June 16-21, 2024 (pp. 4764-4780). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-naacl.296
  • Open Access
    Jumelet, J. (2024). Finding structure in language models. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Jumelet, J., Zuidema, W., & Sinclair, A. (2024). Do Language Models Exhibit Human-like Structural Priming Effects? In L.-W. Ku, A. Martins, & V. Srikumar (Eds.), The 62nd Annual Meeting of the Association for Computational Linguistics : Findings of the Association for Computational Linguistics: ACL 2024: ACL 2024 : August 11-16, 2024 (pp. 14727-14742). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-acl.877
  • Open Access
    Mohebbi, H., Jumelet, J., Hanna, M., Alishahi, A., & Zuidema, W. (2024). Transformer-specific Interpretability. In M. Mesgar, & S. Loáiciga (Eds.), The 18th Conference of the European Chapter of the Association for Computational Linguistics : Proceedings of Tutorial Abstracts: EACL : March 21, 2024 (pp. 21-26). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.eacl-tutorials.4
  • Open Access
    Mohebbi, H., Zuidema, W., Chrupała, G., & Alishahi, A. (2023). Quantifying Context Mixing in Transformers. In A. Vlachos, & I. Augenstein (Eds.), The 17th Conference of the European Chapter of the Association for Computational Linguistics: EACL 2023 : proceedings of the conference : May 2-6, 2023 (pp. 3378-3400). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.eacl-main.245
  • van Dis, E. A. M., Bollen, J., van Rooij, R., Zuidema, W., & Bockting, C. L. (2023). ChatGPT: Five priorities for research. Nature, 614(7947), 223-226. https://doi.org/10.1038/d41586-023-00288-7
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