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
References
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2023
Zhong, W., Xie, Y., & Lin, J. (2023). Answer Retrieval for Math Questions Using Structural and Dense Retrieval Presented at the Retrieval for Math Questions Using Structural and Dense Retrieval Primary Tabs View conference. https://doi.org/10.1007/978-3-031-42448-9_18
Ozsu, T., & Xue, X. (2023). Preface SDA Presented at the Conference Paper Preface SDA conference. Retrieved from https://ceur-ws.org/Vol-3462/SDA0.pdf
Mohapatra, S., Zong, J., Kerschbaum, F., & He, X. (2023). Differentially Private Data Generation With Missing Data ArXiv, abs/2310.11548. https://doi.org/10.48550/ARXIV.2310.11548
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
Ren, H., Mousavi, A., Pacaci, A., Chowdhury, S. R., Mohoney, J., Ilyas, I., … 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
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
Lin, S.-C., Ahmad, A., & Lin, J. (2023). mAggretriever: A Simple Yet Effective Approach to Zero-Shot Multilingual Dense Retrieval Presented at the MAggretriever: A Simple Yet Effective Approach to Zero-Shot Multilingual Dense Retrieval conference. Retrieved from https://aclanthology.org/2023.emnlp-main.715
Tamber, M. S., Pradeep, R., & Lin, J. (2023). Scaling Down, LiTting Up: Efficient Zero-Shot Listwise Reranking With Seq2seq Encoder-Decoder Models ArXiv, abs/2312.16098. https://doi.org/10.48550/ARXIV.2312.16098