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Results: 131
Number of items: 131
  • Open Access
    Cheirmpos, G., Tabatabaei, S. A., Kanoulas, E., & Tsatsaronis, G. (2024). Benchmarking Named Entity Recognition Approaches for Extracting Research Infrastructure Information from Text. In G. Nicosia, V. Ojha, E. La Malfa, G. La Malfa, P. M. Pardalos, & R. Umeton (Eds.), Machine Learning, Optimization, and Data Science: 9th International Conference, LOD 2023, Grasmere, UK, September 22–26, 2023 : revised selected papers (Vol. I, pp. 131–141). (Lecture Notes in Computer Science; Vol. 14505). Springer. https://doi.org/10.1007/978-3-031-53969-5_11
  • Open Access
    Abbasiantaeb, Z., Yuan, Y., Kanoulas, E., & Aliannejadi, M. (2024). Let the LLMs Talk: Simulating Human-to-Human Conversational QA via Zero-Shot LLM-to-LLM Interactions. In WSDM '24: Proceedings of the 17th ACM International Conference on Web Search and Data Mining : March 4-8, 2024, Merida, Mexico (pp. 8-17). Association for Computing Machinery. https://doi.org/10.1145/3616855.3635856
  • Open Access
    Huang, J.-H., Yang, C.-C., Shen, Y., Pacces, A. M., & Kanoulas, E. (2024). Optimizing Numerical Estimation and Operational Efficiency in the Legal Domain through Large Language Models. In CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management : October, 21-25. 2024, Boise, ID, USA (pp. 4554-4562). Association for Computing Machinery. https://doi.org/10.1145/3627673.3680025
  • Open Access
    Ma, H., Zhou, J., Aliannejadi, M., Kanoulas, E., Bin, Y., & Yang, Y. (2024). Ask or Recommend: An Empirical Study on Conversational Product Search. In CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management : October, 21-25. 2024, Boise, ID, USA (pp. 3927-3931). Association for Computing Machinery. https://doi.org/10.1145/3627673.3679875
  • Open Access
    Huang, J.-H., Zhu, H., Shen, Y., Rudinac, S., Pacces, A. M., & Kanoulas, E. (2024). A Novel Evaluation Framework for Image2Text Generation. In C. Siro, M. Aliannejadi, H. A. Rahmani, N. Craswell, C. L. A. Clarke, G. Faggioli, B. Mitra, P. Thomas, & E. Yilmaz (Eds.), Proceedings of The First Workshop on Large Language Models for Evaluation in Information Retrieval (LLM4Eval 2024): co-located with 10th International Conference on Online Publishing (SIGIR 2024) : Washington D.C., USA, July 18, 2024 (pp. 51-65). Article 4 (CEUR Workshop Proceedings; Vol. 3752). CEUR-WS.
  • Open Access
    Soudani, H., Kanoulas, E., & Hasibi, F. (2024). Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge. In SIGIR-AP '24: Proceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region : December 9-12, 2024, Tokyo, Japan (pp. 12-22). Association for Computing Machinery. https://doi.org/10.1145/3673791.3698415
  • Open Access
    Zhu, H., Huang, J.-H., Rudinac, S., & Kanoulas, E. (2024). Enhancing Interactive Image Retrieval With Query Rewriting Using Large Language Models and Vision Language Models. In Proceedings of the 14th Annual ACM International Conference on Multimedia Retrieval (ICMR'24): Phuket, Thailand, June 10-14, 2024 (pp. 978-987). Association for Computing Machinery. https://doi.org/10.1145/3652583.3658032
  • Open Access
    Zou, J., Sun, A., Long, C., & Kanoulas, E. (2024). Knowledge-Enhanced Conversational Recommendation via Transformer-Based Sequential Modeling: ACM Transactions on Information Systems. ACM Transactions on Information Systems, 42(6), Article 162. https://doi.org/10.1145/3677376
  • Open Access
    Pal, V., Kanoulas, E., Yates, A., & de Rijke, M. (2024). Table Question Answering for Low-resourced Indic Languages. 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. 75-92). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.emnlp-main.5
  • Open Access
    Sidiropoulos, G., & Kanoulas, E. (2024). Improving the Robustness of Dense Retrievers Against Typos via Multi-Positive Contrastive Learning. In N. Goharian, N. Tonellotto, Y. He, A. Lipani, G. McDonald, C. Macdonald, & I. Ounis (Eds.), Advances in Information Retrieval: 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24–28, 2024 : proceedings (Vol. III, pp. 297–305). (Lecture Notes in Computer Science; Vol. 14610). Springer. https://doi.org/10.1007/978-3-031-56063-7_21
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