Kamalloo, E. ., Dziri, N. ., Clarke, C. ., & Rafiei, D. . (2023). Evaluating Open-Domain Question Answering in the Era of Large Language Models. ArXiv, abs/2305.06984. https://doi.org/10.48550/arXiv.2305.06984
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Ren, H. ., Mousavi, A. ., Pacaci, A. ., Chowdhury, S. R., Mohoney, J. ., Ilyas, I. ., Li, Y. ., & Rekatsinas, T. . (2023). Fact Ranking Over Large-Scale Knowledge Graphs With Reasoning Embedding Models. IEEE Data Engineering Bulletin, 46, 126-139. Retrieved from http://sites.computer.org/debull/A23june/p126.pdf
Tang, R. ., Zhang, X. ., Ma, X. ., Lin, J. ., & Türe, F. . (2023). Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Models. ArXiv, abs/2310.07712. https://doi.org/10.48550/ARXIV.2310.07712
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 Primary Tabs View. Presented at the. https://doi.org/10.1007/978-3-031-42448-9_18
Zong, S. ., Seltzer, J. ., Pan, J. ., Cheng, K. ., & Lin, J. . (2023). Which Model Shall I Choose? Cost/Quality Trade-Offs for Text Classification Tasks. ArXiv, abs/2301.07006. https://doi.org/10.48550/arXiv.2301.07006
Zhang, C. ., Bonifati, A. ., & Ozsu, T. . (2023). Indexing Techniques for Graph Reachability Queries. ArXiv, abs/2311.03542. https://doi.org/10.48550/ARXIV.2311.03542
Zou, L. ., Pang, Y. ., Ozsu, T. ., & Chen, J. . (2023). Efficient Execution of SPARQL Queries With OPTIONAL and UNION Expressions. ArXiv, abs/2303.13844. https://doi.org/10.48550/arXiv.2303.13844
Ozsu, T. ., & Xue, X. . (2023). Preface SDA. Conference Paper Preface SDA. Presented at the. Retrieved from https://ceur-ws.org/Vol-3462/SDA0.pdf
Mousavi, A. ., Zhan, X. ., Bai, H. ., Shi, P. ., Rekatsinas, T. ., Han, B. ., Li, Y. ., Pound, J. ., Susskind, J. M., Schluter, N. ., Ilyas, I. ., & Jaitly, N. . (2023). Construction of Paired Knowledge Graph-Text Datasets Informed by Cyclic Evaluation. ArXiv, abs/2309.11669. https://doi.org/10.48550/arXiv.2309.11669
Tamber, M. S., Pradeep, R. ., & Lin, J. . (2023). Pre-Processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering. Pre-Processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering. Presented at the. https://doi.org/10.1007/978-3-031-28241-6_11