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Query optimization, which finds the optimized execution plan for a given query, is a complex planning and decision-making problem within the exponentially growing plan space in database management systems (DBMS).
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Learning to Optimize Join Queries With Deep Reinforcement Learning
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The Curious Case of Neural Text Degeneration. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 . OpenReview.net
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Reinforcement Learning with Tree-LSTM for Join Order Selection. In Proc. ICDE . IEEE, 1297–1308
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DSB: A decision support benchmark for workload-driven and traditional database systems
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Bao: Making Learned Query Optimization Practical. In Proc. SIGMOD . ACM, 1275–1288
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Efficient Join Order Selection Learning with Graph-based Representation. In Proc. KDD . ACM, 97–107
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LoRA: Low-Rank Adaptation of Large Language Models. In Proc. ICLR . OpenReview.net
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Direct Preference Optimization: Your Language Model is Secretly a Reward Model. In Proc. NeurIPS
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Code llama: Open foundation models for code
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SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL
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LLaMA: Open and Efficient Foundation Language Models
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Large Language Models are Zero-Shot Reasoners. In Proc. NeurIPS , Sanmi Koyejo, S. Mohamed, A. Agarwal, Danielle Belgrave, K. Cho, and A. Oh (Eds.)
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Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?. In Proc. EMNLP . Association for Computational Linguistics, 11048–11064
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Training language models to follow instructions with human feedback. In Proc. NeurIPS
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Emergent Abilities of Large Language Models
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Balsa: Learning a Query Optimizer Without Expert Demonstrations. In Proc. SIGMOD . ACM, 931–944
Zongheng Yang, Wei-Lin Chiang, Sifei Luan, Gautam Mittal, Michael Luo, and Ion Stoica. 2022 · 2022
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Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection
Xiang Yu, Chengliang Chai, Guoliang Li, and Jiabin Liu. 2022 · 2022
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OPT: Open Pre-trained Transformer Language Models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona T. Diab, Xian Li, Xi Victoria Lin, Todor Mihaylov, Myle Ott, Sam Shleifer, Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, and Luke Zettlemoyer. 2022 · 2022
Cited alongside, same era.
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Demonstrating GPT-DB: Generating Query-Specific and Customizable Code for SQL Processing with GPT-4
Immanuel Trummer. 2023 · 2023
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How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources. In Proc. NeurIPS
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DB-GPT: Empowering Database Interactions with Private Large Language Models
Siqiao Xue, Caigao Jiang, Wenhui Shi, Fangyin Cheng, Keting Chen, Hongjun Yang, Zhiping Zhang, Jianshan He, Hongyang Zhang, Ganglin Wei, Wang Zhao, Fan Zhou, Danrui Qi, Hong Yi, Shaodong Liu, and Faqiang Chen. 2023 · 2023
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Lero: A Learning-to-Rank Query Optimizer
Rong Zhu, Wei Chen, Bolin Ding, Xingguang Chen, Andreas Pfadler, Ziniu Wu, and Jingren Zhou. 2023 · 2023
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Data-Juicer: A One-Stop Data Processing System for Large Language Models. In Companion of the 2024 International Conference on Management of Data, SIGMOD/PODS 2024, Santiago AA, Chile, June 9-15, 2024 . ACM, 120–134
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The Llama 3 Herd of Models
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Demonstrating λ \lambda -Tune: Exploiting Large Language Models for Workload-Adaptive Database System Tuning. In Companion of SIGMOD/PODS . ACM, 508–511
Victor Giannakouris and Immanuel Trummer. 2024 · 2024
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Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL
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A Survey on Large Language Models for Code Generation
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Table-GPT: Table Fine-tuned GPT for Diverse Table Tasks
Peng Li, Yeye He, Dror Yashar, Weiwei Cui, Song Ge, Haidong Zhang, Danielle Rifinski Fainman, Dongmei Zhang, and Surajit Chaudhuri. 2024a · 2024
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LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency
Zhaodonghui Li, Haitao Yuan, Huiming Wang, Gao Cong, and Lidong Bing. 2024b · 2024
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Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study. In Proc. WSDM . ACM, 645–654
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CAESURA: Language Models as Multi-Modal Query Planners. In Proc. CIDR . www.cidrdb.org
Matthias Urban and Carsten Binnig. 2024 · 2024
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Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study
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A Survey of Large Language Models
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