Search results
Results: 92
Number of items: 92
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Gkountouras, J., Lindemann, M., Lippe, P., Gavves, E., & Titov, I. (2025). Language Agents Meet Causality -- Bridging LLMs and Causal World Models. In The Thirteenth International Conference on Learning Representations: ICLR 2025 https://openreview.net/forum?id=y9A2TpaGsE -
Ferreira, P., Titov, I., & Aziz, W. (2025). Explanation Regularisation through the Lens of Attributions. In O. Rambow, L. Wanner, M. Apidianaki, H. Al-Khalifa, B. Di Eugenio, & S. Schockaert (Eds.), The 31st International Conference on Computational Linguistics : proceedings of the main conference: COLING 2025 : January 19-24, 2025 (pp. 6530–6551). Association for Computational Linguistics. https://aclanthology.org/2025.coling-main.436/
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Lindemann, M., Koller, A., & Titov, I. (2024). Strengthening Structural Inductive Biases by Pre-training to Perform Syntactic Transformations. 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. 11558-11573). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.emnlp-main.645
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Dankers, V., & Titov, I. (2024). Generalisation First, Memorisation Second? Memorisation Localisation for Natural Language Classification Tasks. In L.-W. Ku, A. Martins, & V. Srikumar (Eds.), The 62nd Annual Meeting of the Association for Computational Linguistics : Findings of the Association for Computational Linguistics: ACL 2024: ACL 2024 : August 11-16, 2024 (pp. 14348-14366). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-acl.852
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Ramírez, G., Lindemann, M., Birch, A., & Titov, I. (2024). Cache & Distil: Optimising API Calls to Large Language Models. In L.-W. Ku, A. Martins, & V. Srikumar (Eds.), The 62nd Annual Meeting of the Association for Computational Linguistics : Findings of the Association for Computational Linguistics: ACL 2024: ACL 2024 : August 11-16, 2024 (pp. 11838–11853). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-acl.704
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Xu, X., Titov, I., & Lapata, M. (2023). Compositional Generalization for Data-to-Text Generation. 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. 9299-9317). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-emnlp.623
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Züfle, M., Dankers, V., & Titov, I. (2023). Latent Feature-based Data Splits to Improve Generalisation Evaluation: A Hate Speech Detection Case Study. In D. Hupkes, V. Dankers, K. Batsuren, K. Sinha, A. Kazemnejad, C. Christodoulopoulos, R. Cotterell, & E. Bruni (Eds.), GenBench: The first workshop on generalisation (benchmarking) in NLP: GenBench 2023 : Proceedings of the Workshop : December 6, 2023 (pp. 112–129). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.genbench-1.9
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Lindemann, M., Koller, A., & Titov, I. (2023). Compositional Generalisation with Structured Reordering and Fertility Layers. 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. 2172–2186). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.eacl-main.159
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Dankers, V., Titov, I., & Hupkes, D. (2023). Memorisation Cartography: Mapping out the Memorisation-Generalisation Continuum in Neural Machine Translation. 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. 8323-8343). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.518
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