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Results: 9
Number of items: 9
  • Open Access
    Leidinger, A. J. (2025). Towards language models that benefit us all: Studies on stereotypes, robustness, and values. [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
    Pistilli, G., Leidinger, A., Jernite, Y., Kasirzadeh, A., Luccioni, A. S., & Mitchell, M. (2024). CIVICS: Building a Dataset for Examining Culturally-Informed Values in Large Language Models. In S. Das, B. P. Green, K. Varshney, M. Ganapini, & A. Renda (Eds.), Proceedings of the Seventh AAAI/ACM Conference on AI, Ethics, and Society: AIES-24 (pp. 1132-1144). AAAI Press. https://doi.org/10.1609/aies.v7i1.31710
  • Open Access
    Leidinger, A., van Rooij, R., & Shutova, E. (2024). Are LLMs classical or nonmonotonic reasoners? Lessons from generics. In L.-W. Ku, A. Martins, & V. Srikumar (Eds.), The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) : proceedings of the conference: ACL 2024 : August 11-16, 2024 (Vol. 2, pp. 558-573). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.acl-short.51
  • Open Access
    Leidinger, A., & Rogers, R. (2024). How Are LLMs Mitigating Stereotyping Harms? Learning from Search Engine Studies. In S. Das, B. P. Green, K. Varshney, M. Ganapini, & A. Renda (Eds.), Proceedings of the Seventh AAAI/ACM Conference on AI, Ethics, and Society: AIES-24 (pp. 839-854). AAAI Press. https://doi.org/10.1609/aies.v7i1.31684
  • Leidinger, A., & Rogers, R. (2023, May 8). Stereotype elicitation in Google, DuckDuckGo and Yahoo! autcompletion [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7906930
  • Open Access
    Starace, G., Papakostas, K., Choenni, R., Panagiotopoulos, A., Rosati, M., Leidinger, A., & Shutova, E. (2023). Probing LLMs for Joint Encoding of Linguistic Categories. In H. Bouamor, J. Pino, & K. Bali (Eds.), The 2023 Conference on Empirical Methods in Natural Language Processing : Findings of the Association for Computational Linguistics: EMNLP 2023: December 6-10, 2023 (pp. 7158-7179). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-emnlp.476
  • Open Access
    Leidinger, A., & Rogers, R. (2023). Which Stereotypes Are Moderated and Under-Moderated in Search Engine Autocompletion? In FAccT '23: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (pp. 1049–1061). Association for Computing Machinery. https://doi.org/10.1145/3593013.3594062
  • 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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