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Despite the substantial success of Information Retrieval (IR) in various NLP tasks, most IR systems predominantly handle queries and corpora in natural language, neglecting the domain of code retrieval.
Roberta: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Codesearchnet challenge: Evaluating the state of semantic code search
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt. 2019 · 1909
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Okapi at TREC-3
Stephen E. Robertson, Steve Walker, Susan Jones, Micheline Hancock-Beaulieu, and Mike Gatford. 1994 · 1994
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Improved consistent sampling, weighted minhash and l1 sketching
Sergey Ioffe. 2010 · 2010
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A survey on question answering technology from an information retrieval perspective
Oleksandr Kolomiyets and Marie-Francine Moens. 2011 · 2011
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Unsupervised extraction of graph-stream structure for purpose of knowledge retrieval and information fusion
Radoslaw Z. Ziembinski. 2015 · 2015
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MS MARCO: A human generated machine reading comprehension dataset
Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016 · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Introducing visual studio code
Alessandro Del Sole. 2019 · 2019
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CodeBERT: A pre-trained model for programming and natural languages
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, and Ming Zhou. 2020 · 2020
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Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
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Measuring coding challenge competence with apps
Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, and Jacob Steinhardt. 2021 · 2021
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CoSQA: 20,000+ web queries for code search and question answering
Junjie Huang, Duyu Tang, Linjun Shou, Ming Gong, Ke Xu, Daxin Jiang, Ming Zhou, and Nan Duan. 2021 · 2021
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Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2021 · 2021
Cited alongside, same era.
Codexglue: A machine learning benchmark dataset for code understanding and generation
Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin B. Clement, Dawn Drain, Daxin Jiang, Duyu Tang, Ge Li, Lidong Zhou, Linjun Shou, Long Zhou, Michele Tufano, Ming Gong, Ming Zhou, Nan Duan, Neel Sundaresan, Shao Kun Deng, Shengyu Fu, and Shujie Liu. 2021 · 2021
Cited alongside, same era.
BEIR: A heterogeneous benchmark for zero-shot evaluation of information retrieval models
Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, and Iryna Gurevych. 2021 · 2021
Cited alongside, same era.
UniXcoder: Unified cross-modal pre-training for code representation
Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, and Jian Yin. 2022 · 2022
Cited alongside, same era.
Unsupervised dense information retrieval with contrastive learning
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2022 · 2022
Cited alongside, same era.
RepoCoder: Repository-level code completion through iterative retrieval and generation
Fengji Zhang, Bei Chen, Yue Zhang, Jacky Keung, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, and Weizhu Chen. 2023a · 2023
Later among the works it cites.
Syntax-aware retrieval augmented code generation
Xiangyu Zhang, Yu Zhou, Guang Yang, and Taolue Chen. 2023b · 2023
Later among the works it cites.
M3: A multi-task mixed-objective learning framework for open-domain multi-hop dense sentence retrieval
Yang Bai, Anthony M. Colas, Christan Grant, and Daisy Zhe Wang. 2024 · 2024
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Jianlv Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu. 2024 · 2024
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Mc-indexing: Effective long document retrieval via multi-view content-aware indexing
Kuicai Dong, Derrick Goh Xin Deik, Yi Lee, Hao Zhang, Xiangyang Li, Cong Zhang, and Yong Liu. 2024 · 2024
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Inttower: the next generation of two-tower model for pre-ranking system
Xiangyang Li, Bo Chen, HuiFeng Guo, Jingjie Li, Chenxu Zhu, Xiang Long, Sujian Li, Yichao Wang, Wei Guo, Longxia Mao, et al. 2022 · 2022
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Summarization of text and image captioning in information retrieval using deep learning techniques
P. Mahalakshmi and N. Sabiyath Fatima. 2022 · 2022
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Text embeddings by weakly-supervised contrastive pre-training
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 2022 · 2022
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READSUM: retrieval-augmented adaptive transformer for source code summarization
YunSeok Choi, CheolWon Na, Hyojun Kim, and Jee-Hyong Lee. 2023 · 2023
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Mohammad Abdullah Matin Khan, M. Saiful Bari, Xuan Long Do, Weishi Wang, Md. Rizwan Parvez, and Shafiq R. Joty. 2023 · 2023
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Open Information Extraction for Knowledge Representation: Triple Extraction and Information Retrieval From Unstructured Text
Ingy Sarhan. 2023 · 2023
Cited alongside, same era.
C-pack: Packaged resources to advance general chinese embedding
Shitao Xiao, Zheng Liu, Peitian Zhang, and Niklas Muennighof. 2023 · 2023
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Bridging relevance and reasoning: Rationale distillation in retrieval-augmented generation
Pengyue Jia, Derong Xu, Xiaopeng Li, Zhaocheng Du, Xiangyang Li, Xiangyu Zhao, Yichao Wang, Yuhao Wang, Huifeng Guo, and Ruiming Tang. 2024 · 2024
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Ctrla: Adaptive retrieval-augmented generation via probe-guided control
Huanshuo Liu, Hao Zhang, Zhijiang Guo, Kuicai Dong, Xiangyang Li, Yi Quan Lee, Cong Zhang, and Yong Liu. 2024 · 2024
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Synthetic-Text-To-SQL: A synthetic dataset for training language models to generate sql queries from natural language prompts
Yev Meyer, Marjan Emadi, Dhruv Nathawani, Lipika Ramaswamy, Kendrick Boyd, Maarten Van Segbroeck, Matthew Grossman, Piotr Mlocek, and Drew Newberry. 2024 · 2024
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ARKS: active retrieval in knowledge soup for code generation
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Opencodeinterpreter: Integrating code generation with execution and refinement
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How can recommender systems benefit from large language models: A survey
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Process vs. outcome reward: Which is better for agentic rag reinforcement learning
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MTEB: Massive text embedding benchmark
Niklas Muennighoff, Nouamane Tazi, Loic Magne, and Nils Reimers. 2023 · 2037
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