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Retrieval-augmented generation (RAG) is an umbrella of different components, design decisions, and domain-specific adaptations to enhance the capabilities of large language models and counter their limitations regarding hallucination and outdated and missing knowledge.
Why do Internet services fail, and what can be done about it?. In 4th Usenix Symposium on Internet Technologies and Systems (USITS 03)
David Oppenheimer, Archana Ganapathi, and David A Patterson. 2003 · 2003
Earlier work this paper cites.
Static extraction of program configuration options. In Proceedings of the 33rd International Conference on Software Engineering (Waikiki, Honolulu, HI, USA) (ICSE ’11) . Association for Computing Machinery, New York, NY, USA, 131–140
Ariel Rabkin and Randy Katz. 2011 · 2011
Earlier work this paper cites.
An empirical study on configuration errors in commercial and open source systems. In Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles (Cascais, Portugal) (SOSP ’11) . Association for Computing Machinery, New York, NY, USA, 159–172
Zuoning Yin, Xiao Ma, Jing Zheng, Yuanyuan Zhou, Lakshmi N. Bairavasundaram, and Shankar Pasupathy. 2011 · 2011
Earlier work this paper cites.
A user survey of configuration challenges in Linux and eCos. In Proceedings of the 6th International Workshop on Variability Modeling of Software-Intensive Systems (Leipzig, Germany) (VaMoS ’12) . Association for Computing Machinery, New York, NY, USA, 149–155
Arnaud Hubaux, Yingfei Xiong, and Krzysztof Czarnecki. 2012 · 2012
Earlier work this paper cites.
How Hadoop Clusters Break
Ariel Rabkin and Randy Howard Katz. 2013 · 2013
Earlier work this paper cites.
Do not blame users for misconfigurations. In Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles (Farminton, Pennsylvania) (SOSP ’13) . Association for Computing Machinery, New York, NY, USA, 244–259
Tianyin Xu, Jiaqi Zhang, Peng Huang, Jing Zheng, Tianwei Sheng, Ding Yuan, Yuanyuan Zhou, and Shankar Pasupathy. 2013 · 2013
Earlier work this paper cites.
Configurations everywhere: implications for testing and debugging in practice (ICSE Companion 2014) . Association for Computing Machinery, New York, NY, USA, 215–224
Dongpu Jin, Xiao Qu, Myra B. Cohen, and Brian Robinson. 2014 · 2014
Earlier work this paper cites.
EnCore: exploiting system environment and correlation information for misconfiguration detection. In Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems (Salt Lake City, Utah, USA) (ASPLOS ’14) . Association for Computing Machinery, New York, NY, USA, 687–700
Jiaqi Zhang, Lakshminarayanan Renganarayana, Xiaolan Zhang, Niyu Ge, Vasanth Bala, Tianyin Xu, and Yuanyuan Zhou. 2014 · 2014
Earlier work this paper cites.
ConfValley: a systematic configuration validation framework for cloud services. In Proceedings of the Tenth European Conference on Computer Systems (Bordeaux, France) (EuroSys ’15) . Association for Computing Machinery, New York, NY, USA, Article 19, 16 pages
Peng Huang, William J. Bolosky, Abhishek Singh, and Yuanyuan Zhou. 2015 · 2015
Earlier work this paper cites.
Fail at Scale: Reliability in the face of rapid change
Ben Maurer. 2015 · 2015
Earlier work this paper cites.
Holistic configuration management at Facebook (SOSP ’15) . Association for Computing Machinery, New York, NY, USA, 328–343
Chunqiang Tang, Thawan Kooburat, Pradeep Venkatachalam, Akshay Chander, Zhe Wen, Aravind Narayanan, Patrick Dowell, and Robert Karl. 2015 · 2015
Earlier work this paper cites.
Systems Approaches to Tackling Configuration Errors: A Survey
Tianyin Xu and Yuanyuan Zhou. 2015 · 2015
Earlier work this paper cites.
Determine Configuration Entry Correlations for Web Application Systems. In 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC) , Vol. 1. 42–52
Wei Chen, Heng Wu, Jun Wei, Hua Zhong, and Tao Huang. 2016 · 2016
Earlier work this paper cites.
Why Does the Cloud Stop Computing? Lessons from Hundreds of Service Outages. In Proceedings of the Seventh ACM Symposium on Cloud Computing (Santa Clara, CA, USA) (SoCC ’16) . Association for Computing Machinery, New York, NY, USA, 1–16
Haryadi S. Gunawi, Mingzhe Hao, Riza O. Suminto, Agung Laksono, Anang D. Satria, Jeffry Adityatama, and Kurnia J. Eliazar. 2016 · 2016
Earlier work this paper cites.
Probabilistic automated language learning for configuration files. In Computer Aided Verification: 28th International Conference, CAV 2016, Toronto, ON, Canada, July 17-23, 2016, Proceedings, Part II 28 . Springer, 80–87
Mark Santolucito, Ennan Zhai, and Ruzica Piskac. 2016 · 2016
Earlier work this paper cites.
Usable declarative configuration specification and validation for applications, systems, and cloud. In Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference: Industrial Track (Las Vegas, Nevada) (Middleware ’17) . Association for Computing Machinery, New York, NY, USA, 29–35
Salman Baset, Sahil Suneja, Nilton Bila, Ozan Tuncer, and Canturk Isci. 2017 · 2017
Earlier work this paper cites.
Easy over hard: a case study on deep learning. In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering (Paderborn, Germany) (ESEC/FSE 2017) . Association for Computing Machinery, New York, NY, USA, 49–60
Wei Fu and Tim Menzies. 2017 · 2017
Earlier work this paper cites.
Synthesizing configuration file specifications with association rule learning
Mark Santolucito, Ennan Zhai, Rahul Dhodapkar, Aaron Shim, and Ruzica Piskac. 2017 · 2017
Earlier work this paper cites.
On cross-stack configuration errors (ICSE ’17) . IEEE Press, 255–265
Mohammed Sayagh, Noureddine Kerzazi, and Bram Adams. 2017 · 2017
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Earlier work this paper cites.
Language models are few-shot learners
Tom B Brown. 2020 · 2020
Cited alongside, same era.
Understanding and discovering software configuration dependencies in cloud and datacenter systems. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Virtual Event, USA) (ESEC/FSE 2020) . Association for Computing Machinery, New York, NY, USA, 362–374
Qingrong Chen, Teng Wang, Owolabi Legunsen, Shanshan Li, and Tianyin Xu. 2020 · 2020
Cited alongside, same era.
Rex: preventing bugs and misconfiguration in large services using correlated change analysis. In Proceedings of the 17th Usenix Conference on Networked Systems Design and Implementation (Santa Clara, CA, USA) (NSDI’20) . USENIX Association, USA, 435–448
Sonu Mehta, Ranjita Bhagwan, Rahul Kumar, Chetan Bansal, Chandra Maddila, B. Ashok, Sumit Asthana, Christian Bird, and Aditya Kumar. 2020 · 2020
Cited alongside, same era.
CfgNet: A Framework for Tracking Equality-Based Configuration Dependencies Across a Software Project
Sebastian Simon, Nicolai Ruckel, and Norbert Siegmund. 2023 · 2023
Later among the works it cites.
Knowledge editing for large language models: A survey
Song Wang, Yaochen Zhu, Haochen Liu, Zaiyi Zheng, Chen Chen, et al · 2023
Later among the works it cites.
Automated Program Repair in the Era of Large Pre-Trained Language Models. In Proceedings of the 45th International Conference on Software Engineering (Melbourne, Victoria, Australia) (ICSE ’23) . IEEE Press, 1482–1494
Chunqiu Steven Xia, Yuxiang Wei, and Lingming Zhang. 2023 · 2023
Later among the works it cites.
Siren’s song in the AI ocean: a survey on hallucination in large language models
Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, et al · 2023
Later among the works it cites.
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Norbert Siegmund, Nicolai Ruckel, and Janet Siegmund. 2020 · 2020
Cited alongside, same era.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
Cited alongside, same era.
Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI EA ’21) . Association for Computing Machinery, New York, NY, USA, Article 314, 7 pages
Laria Reynolds and Kyle McDonell. 2021 · 2021
Cited alongside, same era.
Large Language Models are Zero-Shot Reasoners
Takeshi Kojima, Shixiang (Shane) Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
Cited alongside, same era.
RACE: Retrieval-augmented Commit Message Generation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing , Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang (Eds.). Association for Computational Linguistics, Abu Dhabi, United Arab Emirates, 5520–5530
Ensheng Shi, Yanlin Wang, Wei Tao, Lun Du, Hongyu Zhang, Shi Han, Dongmei Zhang, and Hongbin Sun. 2022 · 2022
Cited alongside, same era.
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou. 2022 · 2022
Cited alongside, same era.
A systematic evaluation of large language models of code. In Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming (San Diego, CA, USA) (MAPS 2022) . Association for Computing Machinery, New York, NY, USA, 1–10
Frank F. Xu, Uri Alon, Graham Neubig, and Vincent Josua Hellendoorn. 2022 · 2022
Cited alongside, same era.
Large language models are human-level prompt engineers
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. 2022 · 2022
Cited alongside, same era.
Ragas: Automated evaluation of retrieval augmented generation
Shahul Es, Jithin James, Luis Espinosa-Anke, and Steven Schockaert. 2023 · 2023
Cited alongside, same era.
Establishing Traceability between Natural Language Requirements and Software Artifacts by Combining RAG and LLMs
Syed Juned Ali, Varun Naganathan, and Dominik Bork. 2024 · 2024
Closest in time.
Seven Failure Points When Engineering a Retrieval Augmented Generation System. In Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI (Lisbon, Portugal) (CAIN ’24) . Association for Computing Machinery, New York, NY, USA, 194–199
Scott Barnett, Stefanus Kurniawan, Srikanth Thudumu, Zach Brannelly, and Mohamed Abdelrazek. 2024 · 2024
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Benchmarking Large Language Models in Retrieval-Augmented Generation
Jiawei Chen, Hongyu Lin, Xianpei Han, and Le Sun. 2024 · 2024
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LLMTune: Accelerate Database Knob Tuning with Large Language Models
Xinmei Huang, Haoyang Li, Jing Zhang, Xinxin Zhao, Zhiming Yao, Yiyan Li, Zhuohao Yu, Tieying Zhang, Hong Chen, and Cuiping Li. 2024 · 2024
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A Survey on Retrieval-Augmented Text Generation for Large Language Models
Yizheng Huang and Jimmy Huang. 2024 · 2024
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On Mitigating Code LLM Hallucinations with API Documentation
Nihal Jain, Robert Kwiatkowski, Baishakhi Ray, Murali Krishna Ramanathan, and Varun Kumar. 2024 · 2024
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Large language models are zero-shot reasoners (NIPS ’22) . Curran Associates Inc., Article 1613, 15 pages
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2024 · 2024
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Exploring and evaluating hallucinations in llm-powered code generation
Fang Liu, Yang Liu, Lin Shi, Houkun Huang, Ruifeng Wang, Zhen Yang, and Li Zhang. 2024 · 2024
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Test Suite Augmentation using Language Models -Applying RAG to Improve Robustness Verification. In ERTS2024 (ERTS2024) . ERTS2024, Toulouse, France
Adam Mackay. 2024 · 2024
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Evaluating Retrieval Quality in Retrieval-Augmented Generation. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (Washington DC, USA) (SIGIR ’24) . Association for Computing Machinery, New York, NY, USA, 2395–2400
Alireza Salemi and Hamed Zamani. 2024 · 2024
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Searching for Best Practices in Retrieval-Augmented Generation
Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, et al · 2024
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Automated Commit Message Generation with Large Language Models: An Empirical Study and Beyond
Pengyu Xue, Linhao Wu, Zhongxing Yu, Zhi Jin, Zhen Yang, Xinyi Li, Zhenyu Yang, and Yue Tan. 2024 · 2024
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Evaluation of Retrieval-Augmented Generation: A Survey
Hao Yu, Aoran Gan, Kai Zhang, Shiwei Tong, Qi Liu, and Zhaofeng Liu. 2024 · 2024
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RAG-Enhanced Commit Message Generation
Linghao Zhang, Hongyi Zhang, Chong Wang, and Peng Liang. 2024b · 2024
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A comprehensive study of knowledge editing for large language models
Ningyu Zhang, Yunzhi Yao, Bozhong Tian, Peng Wang, Shumin Deng, Mengru Wang, Zekun Xi, Shengyu Mao, Jintian Zhang, Yuansheng Ni, et al · 2024
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Retrieval-Augmented Generation for AI-Generated Content: A Survey
Penghao Zhao, Hailin Zhang, Qinhan Yu, Zhengren Wang, Yunteng Geng, Fangcheng Fu, Ling Yang, Wentao Zhang, and Bin Cui. 2024 · 2024
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