Ilyas, I. ., Lacerda, J. P., Li, Y. ., Minhas, U. F., Mousavi, A. ., Pound, J. ., Rekatsinas, T. ., & Sumanth, C. . (2023). Growing and Serving Large Open-Domain Knowledge Graphs. ArXiv, abs/2305.09464. https://doi.org/10.48550/arXiv.2305.09464
Publications
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Esmaeilzadeh, A. ., Golab, L. ., & Taghva, K. . (2023). InfoMoD: Information-Theoretic Model Diagnostics. Paper InfoMoD: Information-Theoretic Model Diagnostics. Presented at the. https://doi.org/10.1145/3603719.3603725
Thakur, N. ., Ni, J. ., Abrego, G. H. andez \, Wieting, J. ., Lin, J. ., & Cer, D. . (2023). Leveraging LLMs for Synthesizing Training Data Across Many Languages In Multilingual Dense Retrieval. ArXiv, abs/2311.05800. https://doi.org/10.48550/ARXIV.2311.05800
Buchanan, G. R., McKay, D. ., & Clarke, C. . (2023). Made to Measure: A Workshop on Human-Centred Metrics for Information Seeking. Made to Measure: A Workshop on Human-Centred Metrics for Information Seeking. Presented at the. https://doi.org/10.1145/3576840.3578301
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
Mackenzie, J. ., Trotman, A. ., & Lin, J. . (2023). Efficient Document-at-a-Time and Score-at-a-Time Query Evaluation For Learned Sparse Representations. ACM Transactions on Information Systems (TOIS), 41, 1-96. https://doi.org/10.1145/3576922
Pradeep, R. ., Chen, H. ., Gu, L. ., Tamber, M. S., & Lin, J. . (2023). PyGaggle: A Gaggle of Resources for Open-Domain Question Answering. PyGaggle: A Gaggle of Resources for Open-Domain Question Answering. Presented at the. https://doi.org/10.1007/978-3-031-28241-6_10
Zeng, L. ., Zou, L. ., & Ozsu, T. . (2023). SGSI - A Scalable GPU-Friendly Subgraph Isomorphism Algorithm. IEEE Transactions on Knowledge and Data Engineering (TKDE), 35, 11899-11916. https://doi.org/10.1109/TKDE.2022.3230744
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
Lin, J. ., & Teofili, T. . (2023). Searching Dense Representations With Inverted Indexes. ArXiv, abs/2312.01556. https://doi.org/10.48550/ARXIV.2312.01556