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Large Language Models (LLMs) can automate or substitute different types of tasks in the software engineering process.
A query language for analyzing networks
Anton Dries, Siegfried Nijssen, and Luc De Raedt · 2009
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Fundamentals of Database Systems
Ramez Elmasri and Shamkant B. Navathe · 2016
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Provenance for natural language queries
Daniel Deutch, Nerya Frost, and Amir Gilad · 2017
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Rat-sql: Relation-aware schema encoding and linking for text-to-sql parsers
Bailin Wang, Richard Shin, Xiaodong Liu, Oleksandr Polozov, and Matthew Richardson · 2019
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Nadaq: Natural language database querying based on deep learning
Bin Xu, Ruijiang Cai, Zijian Zhang, Xiaochun Yang, Zhifeng Hao, Zhenhui Li, and Zhiqiang Liang · 2019
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Evaluating human-machine translation with attention mechanisms for industry 4.0 environment sql-based systems
Sara Ferreira, Gonçalo Leitão, Igor Silva, Anabela Martins, and Piero Ferrari · 2020
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Measuring and improving consistency in pre-trained language models
Yoav Elazar, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Eduard Hovy, Hinrich Schütze, and Yoav Goldberg · 2021
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Forecasting sql query cost at twitter
Chuan Tang, Bo Wang, Zhenxiao Luo, Huaxin Wu, Sanket Dasan, Min Fu, and Pranav Mishra · 2021
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Natural language to sql queries: A review
Mirza Shahzad Baig, Ali Imran, Abdul Usman Yasin, Arslan Haider Butt, and Muhammad Imran Khan · 2022
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Measuring the carbon intensity of ai in cloud instances
Jesse Dodge, Tess Prewitt, Remi Tachet des Combes, Emily Odmark, Roy Schwartz, Emma Strubell, et al · 2022
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Training compute-optimal large language models
Josh Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Tingfeng Cai, Eliza Rutherford, et al · 2022
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Evaluating the factual consistency of large language models through summarization
Daniel Tam, Sachin Mascarenhas, Sheng Zhang, Stephen Kwan, Mohit Bansal, and Colin Raffel · 2022
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The growing energy footprint of artificial intelligence
Alex de Vries · 2023
Cited alongside, same era.
Odsearch: Fast and resource efficient on-device natural language search for fitness trackers’ data
Reza Rawassizadeh and Yu Rong · 2023
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From words to watts: Benchmarking the energy costs of large language model inference
Siddharth Samsi, Dongfang Zhao, John McDonald, Bo Li, Antonio Michaleas, Michael Jones, et al · 2023
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Enhancing retrieval-augmented large language models with iterative retrieval-generation synergy
Zhezheng Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, and Weizhu Chen · 2023
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Sql-palm: Improved large language model adaptation for text-to-sql
Ruiqi Sun, Sercan O. Arik, Hootan Nakhost, Hang Dai, Rishabh Sinha, Peng Yin, and Tomas Pfister · 2023
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Albert Gu and Tri Dao · 2023
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Making ai less" thirsty": Uncovering and addressing the secret water footprint of ai models
Peifeng Li, Jie Yang, Md Amirul Islam, and Suzhen Ren · 2023
Cited alongside, same era.
Power hungry processing: Watts driving the cost of ai deployment?
Alexandra Sasha Luccioni, Yacine Jernite, and Emma Strubell · 2023
Cited alongside, same era.
Kevin Tian, Eric Mitchell, Huang Yao, Christopher Manning, and Chelsea Finn · 2023
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Long-term memory for large language models through topic-based vector database
Yanzhao Zhang, Zhiwei Yu, Wei Jiang, Yelong Shen, and Jingjing Li · 2023
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https://ml.energy
Ml energy · 2024
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