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Results: 58
Number of items: 58
  • Open Access
    Owers, J., Shutova, E., & Lewis, M. (2024). Density Matrices for Metaphor Understanding. Electronic Proceedings in Theoretical Computer Science, 406, 197-215. https://doi.org/10.4204/EPTCS.406.9
  • Open Access
    Choenni, R., Shutova, E., & Garrette, D. (2024). Examining Modularity in Multilingual LMs via Language-Specialized Subnetworks. In K. Duh, H. Gomez, & S. Bethard (Eds.), Findings of the Association for Computational Linguistics: NAACL 2024: Findings : June 16-21, 2024 (pp. 287-301). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-naacl.21
  • Open Access
    Singh, A. K., Shutova, E., & Yannakoudakis, H. (2024). Learning New Tasks from a Few Examples with Soft-Label Prototypes. In J. Sälevä, & A. Owodunni (Eds.), The 4th Workshop on Multilingual Representation Learning : proceedings of the workshop: MRL 2024 : November 16, 2024 (pp. 215-236). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.repl4nlp-1.16
  • Open Access
    Choenni, R., Lauscher, A., & Shutova, E. (2024). The Echoes of Multilinguality: Tracing Cultural Value Shifts during Language Model Fine-tuning. 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. 15042-15058). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.acl-long.803
  • 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. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2403.11810
  • 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
    Verhoeven, I., Mishra, P., Beloch, R., Yannakoudakis, H., & Shutova, E. (2024). A (More) Realistic Evaluation Setup for Generalisation of Community Models on Malicious Content Detection. 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. 437-463). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-naacl.30
  • Open Access
    van der Heijden, N., Shutova, E., & Yannakoudakis, H. (2023). FewShotTextGCN: K-hop neighborhood regularization for few-shot learning on graphs. 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. 1187-1200). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.eacl-main.85
  • Open Access
    Choenni, R., Garrette, D., & Shutova, E. (2023). How do languages influence each other? Studying cross-lingual data sharing during LM fine-tuning. In H. Bouamor, J. Pino, & K. Bali (Eds.), The 2023 Conference on Empirical Methods in Natural Language Processing: EMNLP 2023 : Proceedings of the Conference : December 6-10, 2023 (pp. 13244-13257). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.818
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