Search results
Results: 57
Number of items: 57
-
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 -
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
-
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
-
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
-
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
-
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
-
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
-
Tedeschi, S., Bos, J., Declerck, T., Hajič, J., Hershcovich, D., Hovy, E. H., Koller, A., Krek, S., Schockaert, S., Sennrich, R., Shutova, E., & Navigli, R. (2023). What's the Meaning of Superhuman Performance in Today's NLU? In A. Rogers, J. Boyd-Graper, & N. Okazaki (Eds.), The 61st Conference of the Association for Computational Linguistics: ACL 2023 : Proceedings of the Conference : July 9-14, 2023 (Vol. 1, pp. 12471-12491). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-long.697
-
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
-
Zhang, Z., Yannakoudakis, H., Zhen, X., & Shutova, E. (2023). CK-Transformer: Commonsense Knowledge Enhanced Transformers for Referring Expression Comprehension. In A. Vlachos, & I. Augenstein (Eds.), The 17th Conference of the European Chapter of the Association for Computational Linguistics : Findings of EACL 2023: EACL 2023 : May 2-6, 2023 (pp. 2586-2596). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-eacl.196
Page 2 of 6