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Results: 57
Number of items: 57
  • Open Access
    Crosbie, J., & Shutova, E. (2025). Induction Heads as an Essential Mechanism for Pattern Matching in In-context Learning. In L. Chiruzzo, A. Ritter, & L. Wang (Eds.), Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics : Proceedings of the Conference : Findings: NAACL 2025 : April 29-May 4, 2025 (pp. 5049–5111). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.findings-naacl.283
  • Open Access
    Yadav, S., Zhang, Z., Hershcovich, D., & Shutova, E. (2025). Beyond Words: Exploring Cultural Value Sensitivity in Multimodal Models. In L. Chiruzzo, A. Ritter, & L. Wang (Eds.), Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics : Proceedings of the Conference : Findings: NAACL 2025 : April 29-May 4, 2025 (pp. 7607–7623). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.findings-naacl.422
  • Open Access
    Choenni, R. M. V. (2025). Multilinguality and multiculturalism: Towards effective and inclusive neural language models. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Zhang, Z. (2025). Advancing vision and language models through commonsense knowledge, efficient adaptation and transparency. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Rajaee, S., Choenni, R., Shutova, E., & Monz, C. (2025). An Empirical Analysis of Machine Translation for Expanding Multilingual Benchmarks. In B. Haddow, T. Kocmi, P. Koehn, & C. Monz (Eds.), Tenth Conference on Machine Translation : Proceedings of the Conference: WMT 2025 : November 8-9, 2025 (pp. 1-30). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.wmt-1.1
  • Open Access
    Johnson, T., ter Veen, M., Choenni, R., van der Maas, H. L. J., Shutova, E., & Stevenson, C. E. (2025). Do large language models solve verbal analogies like children do? In G. Boleda, & M. Roth (Eds.), The 29th Conference on Computational Natural Language Learning (CoNLL 2025) : Proceedings of the Conference: CoNLL 2025 : July 31-August 1, 2025 (pp. 627-639). Association for Computational Linguistics. https://doi.org/10.48550/arXiv.2310.20384, https://doi.org/10.18653/v1/2025.conll-1.40
  • 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].
  • Verhoeven, I., Mishra, P., & Shutova, E. (2024). misinfo-general [Data set]. Harvard Dataverse. https://doi.org/10.7910/dvn/txxufn
  • Open Access
    Zhang, Z., Zhang, Q., Gao, Z., Zhang, R., Shutova, E., Zhou, S., & Zhang, S. (2024). Gradient-based Parameter Selection for Efficient Fine-Tuning. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition: CVPR 2024 : Seattle, Washington, USA, 16-22 June 2024 : proceedings (pp. 28566-28577). IEEE Computer Society. https://doi.org/10.48550/arXiv.2312.10136, https://doi.org/10.1109/CVPR52733.2024.02699
  • Open Access
    Tong, X., Choenni, R., Lewis, M., & Shutova, E. (2024). Metaphor Understanding Challenge Dataset for LLMs. 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. 1, pp. 3517-3536). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.acl-long.193
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