Pradeep, R., Hui, K., Gupta, J., Lelkes, A. am D., Zhuang, H., Lin, J., … Tran, V. Q. (2023). How Does Generative Retrieval Scale to Millions of Passages? Presented at the How Does Generative Retrieval Scale to Millions of Passages? conference. Retrieved from https://aclanthology.org/2023.emnlp-main.83
References
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Ozsu, T. (2023). Data Science: A Systematic Treatment ArXiv, abs/2301.13761. https://doi.org/10.48550/arXiv.2301.13761
Qian, K., Belyi, A., Wu, F., Khorshidi, S., Nikfarjam, A., Khot, R., … Li, Y. (2023). Open Domain Knowledge Extraction for Knowledge Graphs ArXiv, abs/2312.09424. https://doi.org/10.48550/ARXIV.2312.09424
Dadvar, V., Golab, L., & Srivastava, D. (2023). POEM: Pattern-Oriented Explanations of Convolutional Neural Networks Proceedings of the VLDB Endowment (PVLDB), 16, 3192-3200. https://doi.org/10.14778/3611479.3611518
Conia, S., Li, M., Lee, D., Minhas, U. F., Ilyas, I., & Li, Y. (2023). Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs Presented at the Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs conference. Retrieved from https://aclanthology.org/2023.emnlp-main.100
Huang, C., Xie, Y., Jiang, Z., Lin, J., & Li, M. (2023). Approximating Human-Like Few-Shot Learning With GPT-based Compression ArXiv, abs/2308.06942. https://doi.org/10.48550/arXiv.2308.06942
Faggioli, G., Dietz, L., Clarke, C., Demartini, G., Hagen, M., Hauff, C., … Wachsmuth, H. (2023). Perspectives on Large Language Models for Relevance Judgment ArXiv, abs/2304.09161. https://doi.org/10.48550/arXiv.2304.09161
Usta, A., & Salihoglu, S. (2023). To Join or Not to Join: An Analysis on the Usefulness of Joining Tables In Open Government Data Portals Presented at the To Join or Not to Join: An Analysis on the Usefulness of Joining Tables In Open Government Data Portals conference. Retrieved from https://ceur-ws.org/Vol-3462/TADA5.pdf
Tang, R., Zhang, X., Lin, J., & Türe, F. (2023). What Do Llamas Really Think? Revealing Preference Biases in Language Model Representations ArXiv, abs/2311.18812. https://doi.org/10.48550/ARXIV.2311.18812
Ma, X., Fun, H., Yin, X., Mallia, A., & Lin, J. (2023). Enhancing Sparse Retrieval via Unsupervised Learning Presented at the Enhancing Sparse Retrieval via Unsupervised Learning conference. https://doi.org/10.1145/3624918.3625334