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Results: 29
Number of items: 29
  • Open Access
    Qi, J., Fernández, R., & Bisazza, A. (2025). On the Consistency of Multilingual Context Utilization in Retrieval-Augmented Generation. In D. I. Adelani, C. Arnett, D. Ataman, T. A. Chang, H. Gonen, R. Raja, F. Schmidt, D. Stap, & J. Wang (Eds.), The 5th Workshop on Multilingual Representation Learning (MRL 2025) : proceedings of the workshop: MRL 2025 : November 8-9, 2025 (pp. 199–225). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.mrl-main.15
  • Open Access
    Neplenbroek, V., Bisazza, A., & Fernández, R. (2025). Cross-Lingual Transfer of Debiasing and Detoxification in Multilingual LLMs: An Extensive Investigation. In W. Che, J. Nabende, E. Shutova, & M. T. Pilehvar (Eds.), The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025) : Findings of the Association for Computational Linguistics: ACL 2025: ACL 2025 : July 27-August 1, 2025 (pp. 2805-2830). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.findings-acl.145
  • Open Access
    Neplenbroek, V., Bisazza, A., & Fernández, R. (2025). Reading Between the Prompts: How Stereotypes Shape LLM’s Implicit Personalization. In C. Christodoulopoulos, T. Chakraborty, C. Rose, & V. Peng (Eds.), The 2025 Conference on Empirical Methods in Natural Language Processing : Proceedings of the Conference: EMNLP 2025 : November 4-9, 2025 (pp. 20367-20400). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.emnlp-main.1029
  • Open Access
    Qi, J., Chen, S., Xiong, Z., Fernández, R., Bitterman, D. S., & Bisazza, A. (2025). When Models Reason in Your Language: Controlling Thinking Language Comes at the Cost of Accuracy. In C. Christodoulopoulos, T. Chakraborty, C. Rose, & V. Peng (Eds.), The 2025 Conference on Empirical Methods in Natural Language Processing : Findings of EMNLP 2025: EMNLP 2025 : November 4-9, 2025 (pp. 20279–20296). Association for Computational Linguistics. https://aclanthology.org/2025.findings-emnlp.1103/
  • Open Access
    Shen, G., Watkins, M., Alishahi, A., Bisazza, A., & Chrupała, G. (2024). Encoding of lexical tone in self-supervised models of spoken language. In K. Duh, H. Gomez, & S. Bethard (Eds.), The 2024 Conference of the North American Chapter of the Association for Computational Linguistics : proceedings of the conference: NAACL 2024 : June 16-21, 2024 (Vol. 1, pp. 4250-4261). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.naacl-long.239
  • Open Access
    Qi, J., Sarti, G., Fernández, R., & Bisazza, A. (2024). Model Internals-based Answer Attribution for Trustworthy Retrieval-Augmented Generation. In Y. Al-Onaizan, M. Bansal, & Y.-N. Chen (Eds.), The 2024 Conference on Empirical Methods in Natural Language Processing : Proceedings of the Conference: EMNLP 2024 : November 12-16, 2024 (pp. 6037-6053). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.emnlp-main.347
  • Open Access
    Chen, X., Liao, B., Qi, J., Eustratiadis, P., Monz, C., Bisazza, A., & de Rijke, M. (2024). The SIFo Benchmark: Investigating the Sequential Instruction Following Ability of Large Language Models. In Y. Al-Onaizan, M. Bansal, & Y.-N. Chen (Eds.), The 2024 Conference on Empirical Methods in Natural Language Processing : Findings of EMNLP 2024: EMNLP 2024 : November 12-16, 2024 (pp. 1691-1706). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-emnlp.92
  • Open Access
    Qi, J., Fernández, R., & Bisazza, A. (2023). Cross-Lingual Consistency of Factual Knowledge in Multilingual Language Models. In H. Bouamar, 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. 10650-10666). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.658
  • Open Access
    Fadaee, M. (2020). Understanding and enhancing the use of context for machine translation. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Tran, K., Bisazza, A., & Monz, C. (2018). The Importance of Being Recurrent for Modeling Hierarchical Structure. In E. Riloff, D. Chiang, J. Hockenmaier, & J. Tsujii (Eds.), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing : EMNLP 2018: Brussels, Belgium, Oct. 31-Nov. 4 (pp. 4731–4736). The Association for Computational Linguistics. https://doi.org/10.18653/v1/D18-1503
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