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Retrieval-Augmented Generation (RAG) has emerged as a critical technique for enhancing large language model (LLM) capabilities.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
Earlier work this paper cites.
The probabilistic relevance framework: BM25 and beyond
Stephen Robertson, Hugo Zaragoza, et al · 2009
Earlier work this paper cites.
Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 1601–1611
Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. 2017 · 2017
Earlier work this paper cites.
HotpotQA: A dataset for diverse, explainable multi-hop question answering. In Proceedings of the 2018 conference on empirical methods in natural language processing . 2369–2380
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018 · 2018
Earlier work this paper cites.
Learning to mine aligned code and natural language pairs from stack overflow. In Proceedings of the 15th international conference on mining software repositories . 476–486
Pengcheng Yin, Bowen Deng, Edgar Chen, Bogdan Vasilescu, and Graham Neubig. 2018 · 2018
Earlier work this paper cites.
An empirical study on tensorflow program bugs. In Proceedings of the 27th ACM SIGSOFT international symposium on software testing and analysis . 129–140
Yuhao Zhang, Yifan Chen, Shing-Chi Cheung, Yingfei Xiong, and Lu Zhang. 2018 · 2018
Earlier work this paper cites.
Software engineering for machine learning: A case study. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) . IEEE, 291–300
Saleema Amershi, Andrew Begel, Christian Bird, Robert DeLine, Harald Gall, Ece Kamar, Nachiappan Nagappan, Besmira Nushi, and Thomas Zimmermann. 2019 · 2019
Earlier work this paper cites.
A comprehensive study on deep learning bug characteristics. In Proceedings of the 2019 27th ACM joint meeting on european software engineering conference and symposium on the foundations of software engineering . 510–520
Md Johirul Islam, Giang Nguyen, Rangeet Pan, and Hridesh Rajan. 2019 · 2019
Earlier work this paper cites.
Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al · 2019
Earlier work this paper cites.
Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP) . 3982–3992
Nils Reimers and Iryna Gurevych. 2019 · 2019
Earlier work this paper cites.
An empirical study of common challenges in developing deep learning applications. In 2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE) . IEEE, 104–115
Tianyi Zhang, Cuiyun Gao, Lei Ma, Michael Lyu, and Miryung Kim. 2019 · 2019
Earlier work this paper cites.
A comprehensive study on challenges in deploying deep learning based software. In Proceedings of the 28th ACM joint meeting on European software engineering conference and symposium on the foundations of software engineering . 750–762
Zhenpeng Chen, Yanbin Cao, Yuanqiang Liu, Haoyu Wang, Tao Xie, and Xuanzhe Liu. 2020 · 2020
Earlier work this paper cites.
Taxonomy of real faults in deep learning systems. In Proceedings of the ACM/IEEE 42nd international conference on software engineering . 1110–1121
Nargiz Humbatova, Gunel Jahangirova, Gabriele Bavota, Vincenzo Riccio, Andrea Stocco, and Paolo Tonella. 2020 · 2020
Earlier work this paper cites.
Dense passage retrieval for open-domain question answering. In Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP) . 6769–6781
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
Earlier work this paper cites.
Adoption and effects of software engineering best practices in machine learning. In Proceedings of the 14th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) . 1–12
Alex Serban, Koen Van der Blom, Holger Hoos, and Joost Visser. 2020 · 2020
Earlier work this paper cites.
An empirical study on program failures of deep learning jobs. In Proceedings of the ACM/IEEE 42nd international conference on software engineering . 1159–1170
Ru Zhang, Wencong Xiao, Hongyu Zhang, Yu Liu, Haoxiang Lin, and Mao Yang. 2020 · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
An empirical study on deployment faults of deep learning based mobile applications. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 674–685
Zhenpeng Chen, Huihan Yao, Yiling Lou, Yanbin Cao, Yuanqiang Liu, Haoyu Wang, and Xuanzhe Liu. 2021b · 2021
Earlier work this paper cites.
An empirical study on common bugs in deep learning compilers. In 2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE) . IEEE, 184–195
Xiaoting Du, Zheng Zheng, Lei Ma, and Jianjun Zhao. 2021 · 2021
Earlier work this paper cites.
Asset management in machine learning: A survey. In 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) . IEEE, 51–60
Samuel Idowu, Daniel Strüber, and Thorsten Berger. 2021 · 2021
Earlier work this paper cites.
Unsupervised Dense Information Retrieval with Contrastive Learning
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2021 · 2021
Earlier work this paper cites.
Pyserini: A Python toolkit for reproducible information retrieval research with sparse and dense representations. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 2356–2362
Jimmy Lin, Xueguang Ma, Sheng-Chieh Lin, Jheng-Hong Yang, Ronak Pradeep, and Rodrigo Nogueira. 2021 · 2021
Earlier work this paper cites.
Codexglue: A machine learning benchmark dataset for code understanding and generation
Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin Clement, Dawn Drain, Daxin Jiang, Duyu Tang, et al · 2021
Earlier work this paper cites.
A comprehensive study of deep learning compiler bugs. In Proceedings of the 29th ACM Joint meeting on european software engineering conference and symposium on the foundations of software engineering . 968–980
Qingchao Shen, Haoyang Ma, Junjie Chen, Yongqiang Tian, Shing-Chi Cheung, and Xiang Chen. 2021 · 2021
Earlier work this paper cites.
Are machine learning cloud apis used correctly?. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 125–137
Chengcheng Wan, Shicheng Liu, Henry Hoffmann, Michael Maire, and Shan Lu. 2021 · 2021
Earlier work this paper cites.
Understanding performance problems in deep learning systems. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 357–369
Junming Cao, Bihuan Chen, Chao Sun, Longjie Hu, Shuaihong Wu, and Xin Peng. 2022 · 2022
Earlier work this paper cites.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
Earlier work this paper cites.
Reacc: A retrieval-augmented code completion framework. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 6227–6240
Shuai Lu, Nan Duan, Hojae Han, Daya Guo, Seung-won Hwang, and Alexey Svyatkovskiy. 2022 · 2022
Earlier work this paper cites.
Collaboration challenges in building ml-enabled systems: Communication, documentation, engineering, and process. In Proceedings of the 44th international conference on software engineering . 413–425
Nadia Nahar, Shurui Zhou, Grace Lewis, and Christian Kästner. 2022 · 2022
Earlier work this paper cites.
OPENAI. 2022
2022
Earlier work this paper cites.
Challenges in deploying machine learning: a survey of case studies
Andrei Paleyes, Raoul-Gabriel Urma, and Neil D Lawrence. 2022 · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Cited alongside, same era.
CERT: Continual Pre-training on Sketches for Library-oriented Code Generation. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence . International Joint Conferences on Artificial Intelligence Organization, 2369–2375
Daoguang Zan, Bei Chen, Dejian Yang, Zeqi Lin, Minsu Kim, Bei Guan, Yongji Wang, Weizhu Chen, and Jian-Guang Lou. 2022 · 2022
Cited alongside, same era.
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. In The Eleventh International Conference on Learning Representations
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V Le, et al · 2022
Cited alongside, same era.
Acecoder: An effective prompting technique specialized in code generation
Jia Li, Yunfei Zhao, Yongmin Li, Ge Li, and Zhi Jin. 2024b · 2024
Closest in time.
Lost in the middle: How language models use long contexts
Nelson F Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2024a · 2024
Closest in time.
Wei Liu, Ailun Yu, Daoguang Zan, Bo Shen, Wei Zhang, Haiyan Zhao, Zhi Jin, and Qianxiang Wang. 2024b · 2024
Closest in time.
Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al · 2024
Closest in time.
OpenAI. 2024
2024
Closest in time.
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Junjie Chen, Yihua Liang, Qingchao Shen, Jiajun Jiang, and Shuochuan Li. 2023 · 2023
Cited alongside, same era.
Retrieval-augmented generation for large language models: A survey
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang. 2023 · 2023
Cited alongside, same era.
Understanding and mitigating hardware failures in deep learning training systems. In Proceedings of the 50th Annual International Symposium on Computer Architecture . 1–16
Yi He, Mike Hutton, Steven Chan, Robert De Gruijl, Rama Govindaraju, Nishant Patil, and Yanjing Li. 2023 · 2023
Cited alongside, same era.
Active retrieval augmented generation. In Proceedings of the 2023 conference on empirical methods in natural language processing . 7969–7992
Zhengbao Jiang, Frank F Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, and Graham Neubig. 2023 · 2023
Cited alongside, same era.
DS-1000: A natural and reliable benchmark for data science code generation. In International Conference on Machine Learning . PMLR, 18319–18345
Yuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang, Ruiqi Zhong, Luke Zettlemoyer, Wen-tau Yih, Daniel Fried, Sida Wang, and Tao Yu. 2023 · 2023
Cited alongside, same era.
Large language models understand and can be enhanced by emotional stimuli
Cheng Li, Jindong Wang, Yixuan Zhang, Kaijie Zhu, Wenxin Hou, Jianxun Lian, Fang Luo, Qiang Yang, and Xing Xie. 2023b · 2023
Cited alongside, same era.
Skcoder: A sketch-based approach for automatic code generation. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2124–2135
Jia Li, Yongmin Li, Ge Li, Zhi Jin, Yiyang Hao, and Xing Hu. 2023a · 2023
Cited alongside, same era.
Codegen4libs: A two-stage approach for library-oriented code generation. In 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 434–445
Mingwei Liu, Tianyong Yang, Yiling Lou, Xueying Du, Ying Wang, and Xin Peng. 2023 · 2023
Cited alongside, same era.
Dongyu Ru, Lin Qiu, Xiangkun Hu, Tianhang Zhang, Peng Shi, Shuaichen Chang, Cheng Jiayang, Cunxiang Wang, Shichao Sun, Huanyu Li, et al · 2024
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A systematic survey of prompt engineering in large language models: Techniques and applications
Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, and Aman Chadha. 2024 · 2024
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The Prompt Report: A Systematic Survey of Prompting Techniques
Sander Schulhoff, Michael Ilie, Nishant Balepur, Konstantine Kahadze, Amanda Liu, Chenglei Si, Yinheng Li, Aayush Gupta, HyoJung Han, Sevien Schulhoff, et al · 2024
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A survey of prompt engineering methods in large language models for different nlp tasks
Shubham Vatsal and Harsh Dubey. 2024 · 2024
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Unims-rag: A unified multi-source retrieval-augmented generation for personalized dialogue systems
Hongru Wang, Wenyu Huang, Yang Deng, Rui Wang, Zezhong Wang, Yufei Wang, Fei Mi, Jeff Z Pan, and Kam-Fai Wong. 2024b · 2024
Closest in time.
Searching for best practices in retrieval-augmented generation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing . 17716–17736
Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, et al · 2024
Closest in time.
REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen (Eds.). Association for Computational Linguistics, Miami, Florida, USA, 5613–5626
Yuhao Wang, Ruiyang Ren, Junyi Li, Xin Zhao, Jing Liu, and Ji-Rong Wen. 2024c · 2024
Closest in time.
Coderag-bench: Can retrieval augment code generation?
Zora Zhiruo Wang, Akari Asai, Xinyan Velocity Yu, Frank F Xu, Yiqing Xie, Graham Neubig, and Daniel Fried. 2024a · 2024
Closest in time.
Benchmarking retrieval-augmented generation for medicine. In Findings of the Association for Computational Linguistics ACL 2024 . 6233–6251
Guangzhi Xiong, Qiao Jin, Zhiyong Lu, and Aidong Zhang. 2024 · 2024
Closest in time.
Chain-of-note: Enhancing robustness in retrieval-augmented language models. In Proceedings of the 2024 conference on empirical methods in natural language processing . 14672–14685
Wenhao Yu, Hongming Zhang, Xiaoman Pan, Peixin Cao, Kaixin Ma, Jian Li, Hongwei Wang, and Dong Yu. 2024b · 2024
Closest in time.
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Siyun Zhao, Yuqing Yang, Zilong Wang, Zhiyuan He, Luna K. Qiu, and Lili Qiu. 2024 · 2024
Closest in time.
AIBusiness. 2025
2025
Closest in time.
Engineering rag systems for real-world applications: Design, development, and evaluation. In Euromicro Conference on Software Engineering and Advanced Applications . Springer, 143–158
Md Toufique Hasan, Muhammad Waseem, Kai-Kristian Kemell, Ayman Asad Khan, Mika Saari, and Pekka Abrahamsson. 2025 · 2025
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Look before you leap: An exploratory study of uncertainty analysis for large language models
Yuheng Huang, Jiayang Song, Zhijie Wang, Shengming Zhao, Huaming Chen, Felix Juefei-Xu, and Lei Ma. 2025 · 2025
Closest in time.
Rago: Systematic performance optimization for retrieval-augmented generation serving. In Proceedings of the 52nd Annual International Symposium on Computer Architecture . 974–989
Wenqi Jiang, Suvinay Subramanian, Cat Graves, Gustavo Alonso, Amir Yazdanbakhsh, and Vidushi Dadu. 2025 · 2025
Closest in time.
Medium. 2025
2025
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MIRAGE: A Metric-Intensive Benchmark for Retrieval-Augmented Generation Evaluation. In Findings of the Association for Computational Linguistics: NAACL 2025 , Luis Chiruzzo, Alan Ritter, and Lu Wang (Eds.). Association for Computational Linguistics, Albuquerque, New Mexico, 2883–2900
Chanhee Park, Hyeonseok Moon, Chanjun Park, and Heuiseok Lim. 2025 · 2025
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A Large-Scale Study of Model Integration in ML-Enabled Software Systems. In Proceedings of the 47th International Conference on Software Engineering (ICSE) (Ottawa, Ontario, Canada)
Yorick Sens, Henriette Knopp, Sven Peldszus, and Thorsten Berger. 2025 · 2025
Closest in time.
Are LLMs Correctly Integrated into Software Systems?. In 2025 IEEE/ACM 47th International Conference on Software Engineering (ICSE) . IEEE Computer Society, 741–741
Yuchen Shao, Yuheng Huang, Jiawei Shen, Lei Ma, Ting Su, and Chengcheng Wan. 2025 · 2025
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Calibration and correctness of language models for code. In 2025 IEEE/ACM 47th International Conference on Software Engineering (ICSE) . IEEE, 540–552
Claudio Spiess, David Gros, Kunal Suresh Pai, Michael Pradel, Md Rafiqul Islam Rabin, Amin Alipour, Susmit Jha, Prem Devanbu, and Toufique Ahmed. 2025 · 2025
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DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented Generation. In The Thirty-ninth Annual Conference on Neural Information Processing Systems
Jiashuo Sun, Xianrui Zhong, Sizhe Zhou, and Jiawei Han. 2025 · 2025
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Prompting techniques for secure code generation: A systematic investigation
Catherine Tony, Nicolás E Díaz Ferreyra, Markus Mutas, Salem Dhif, and Riccardo Scandariato. 2025 · 2025
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Analytics Vidhya. 2025
2025
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Yuchen Wang, Shangxin Guo, and Chee Wei Tan. 2025 · 2025
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