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Zhong, W. ., Xie, Y. ., & Lin, J. . (2023). Answer Retrieval for Math Questions Using Structural and Dense Retrieval. Retrieval for Math Questions Using Structural and Dense Retrieval. Presented at the. https://doi.org/10.1007/978-3-031-42448-9_18

Li, M. ., Lin, S.-C. ., Ma, X. ., & Lin, J. . (2023). SLIM: Sparsified Late Interaction for Multi-Vector Retrieval With Inverted Indexes. ArXiv, abs/2302.06587. https://doi.org/10.48550/arXiv.2302.06587

Hildred, J. ., Abebe, M. ., & Daudjee, K. . (2023). Caerus: Low-Latency Distributed Transactions for Geo-Replicated Systems. Proceedings of the VLDB Endowment (PVLDB), 17, 469-482. Retrieved from https://www.vldb.org/pvldb/vol17/p469-hildred.pdf

Ma, X. ., Teofili, T. ., & Lin, J. . (2023). Anserini Gets Dense Retrieval: Integration of Lucene\textquoterights HNSW Indexes. ArXiv, abs/2304.12139. https://doi.org/10.48550/arXiv.2304.12139

Li, M. ., Zhuang, H. ., Hui, K. ., Qin, Z. ., Lin, J. ., Jagerman, R. ., Wang, X. ., & Bendersky, M. . (2023). Generate, Filter, and Fuse: Query Expansion via Multi-Step Keyword Generation for Zero-Shot Neural Rankers. ArXiv, abs/2311.09175. https://doi.org/10.48550/ARXIV.2311.09175

Hildred, J. ., Abebe, M. ., & Daudjee, K. . (2023). Caerus: Low-Latency Distributed Transactions for Geo-Replicated Systems. Proceedings of the VLDB Endowment (PVLDB), 17, 469-482. Retrieved from https://www.vldb.org/pvldb/vol17/p469-hildred.pdf

Hebert, L. ., Chen, H. Y., Cohen, R. ., & Golab, L. . (2023). Qualitative Analysis of a Graph Transformer Approach to Addressing Hate Speech: Adapting to Dynamically Changing Content. ArXiv, abs/2301.10871. https://doi.org/10.48550/arXiv.2301.10871

Lin, S.-C. ., Asai, A. ., Li, M. ., Oguz, B. ., Lin, J. ., Mehdad, Y. ., Yih, W.- tau ., & Chen, X. . (2023). How to Train Your Dragon: Diverse Augmentation Towards Generalizable Dense Retrieval. How to Train Your Dragon: Diverse Augmentation Towards Generalizable Dense Retrieval. Presented at the. Retrieved from https://aclanthology.org/2023.findings-emnlp.423

Hebert, L. ., Golab, L. ., Poupart, P. ., & Cohen, R. . (2023). FedFormer: Contextual Federation With Attention in Reinforcement Learning. FedFormer: Contextual Federation With Attention in Reinforcement Learning. Presented at the. https://doi.org/10.5555/3545946.3598716

Lin, S.-C. ., & Lin, J. . (2023). A Dense Representation Framework for Lexical and Semantic Matching. ACM Transactions on Information Systems (TOIS), 41, 1-110. https://doi.org/10.1145/3582426