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Despite the success of text retrieval in many NLP tasks, code retrieval remains a largely underexplored area.
A theoretical analysis of ndcg type ranking measures
Yining Wang, Liwei Wang, Yuanzhi Li, Di He, and Tie-Yan Liu · 2013
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Evaluating clone detection tools with bigclonebench
Jeffrey Svajlenko and Chanchal K Roy · 2015
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Ms marco: A human generated machine reading comprehension dataset
Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, et al · 2016
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Seq2sql: Generating structured queries from natural language using reinforcement learning
Victor Zhong, Caiming Xiong, and Richard Socher · 2017
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Improving text-to-sql evaluation methodology
Catherine Finegan-Dollak, Jonathan K Kummerfeld, Li Zhang, Karthik Ramanathan, Sesh Sadasivam, Rui Zhang, and Dragomir Radev · 2018
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Summarizing source code with transferred api knowledge
Xing Hu, Ge Li, Xin Xia, David Lo, Shuai Lu, and Zhi Jin · 2018
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Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task
Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, et al · 2018
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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
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 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, et al · 2020
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Retrieval-augmented generation for code summarization via hybrid gnn
Shangqing Liu, Yu Chen, Xiaofei Xie, Jing Kai Siow, and Yang Liu · 2020
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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
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Introducing visual studio code
Alessandro Del Sole · 2021
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Scaling deep contrastive learning batch size under memory limited setup
Luyu Gao, Yunyi Zhang, Jiawei Han, and Jamie Callan · 2021
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Towards unsupervised dense information retrieval with contrastive learning
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave · 2021
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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
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Retrieval augmented code generation and summarization
Md Rizwan Parvez, Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang · 2021
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Beir: A heterogeneous benchmark for zero-shot evaluation of information retrieval models
Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, and Iryna Gurevych · 2021
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Recent advances in retrieval-augmented text generation
Deng Cai, Yan Wang, Lemao Liu, and Shuming Shi · 2022
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Why do we need large batchsizes in contrastive learning? a gradient-bias perspective
Changyou Chen, Jianyi Zhang, Yi Xu, Liqun Chen, Jiali Duan, Yiran Chen, Son Tran, Belinda Zeng, and Trishul Chilimbi · 2022
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Unixcoder: Unified cross-modal pre-training for code representation
Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, and Jian Yin · 2022
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Lora: Low-rank adaptation of large language models
Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al · 2022
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Text and code embeddings by contrastive pre-training
Arvind Neelakantan, Tao Xu, Raul Puri, Alec Radford, Jesse Michael Han, Jerry Tworek, Qiming Yuan, Nikolas Tezak, Jong Wook Kim, Chris Hallacy, et al · 2022
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Code summarization: Do transformers really understand code?
Ankita Nandkishor Sontakke, Manasi Patwardhan, Lovekesh Vig, Raveendra Kumar Medicherla, Ravindra Naik, and Gautam Shroff · 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
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Unsupervised dense information retrieval with contrastive learning
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave · 2023
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Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al · 2023
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Ds-1000: A natural and reliable benchmark for data science code generation
Yuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang, Ruiqi Zhong, Luke Zettlemoyer, Wen-tau Yih, Daniel Fried, Sida Wang, and Tao Yu · 2023
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Codegen2: Lessons for training llms on programming and natural languages
Teach ai how to code: Using large language models as teachable agents for programming education
Hyoungwook Jin, Seonghee Lee, Hyungyu Shin, and Juho Kim · 2024
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Exploring the competency of chatgpt in solving competitive programming challenges
Md Eusha Kadir, Tasnim Rahman, Sourav Barman, and Md Al-Amin · 2024
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Solmover: Smart contract code translation based on concepts
Rabimba Karanjai, Lei Xu, and Weidong Shi · 2024
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Xcodeeval: An execution-based large scale multilingual multitask benchmark for code understanding, generation, translation and retrieval
Mohammad Abdullah Matin Khan, M Saiful Bari, Do Long, Weishi Wang, Md Rizwan Parvez, and Shafiq Joty · 2024
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Nv-embed: Improved techniques for training llms as generalist embedding models
Chankyu Lee, Rajarshi Roy, Mengyao Xu, Jonathan Raiman, Mohammad Shoeybi, Bryan Catanzaro, and Wei Ping · 2024
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Erik Nijkamp, Hiroaki Hayashi, Caiming Xiong, Silvio Savarese, and Yingbo Zhou · 2023
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Code llama: Open foundation models for code
Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Romain Sauvestre, Tal Remez, et al · 2023
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Redpajama-data-v2: An open dataset with 30 trillion tokens for training large language models, october 2023
Together AI Team et al · 2023
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Improving text embeddings with large language models
Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, and Furu Wei · 2023
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Using github copilot to solve simple programming problems
Michel Wermelinger · 2023
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C-pack: Packaged resources to advance general chinese embedding
Shitao Xiao, Zheng Liu, Peitian Zhang, and Niklas Muennighof · 2023
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Burak Yetiştiren, Işık Özsoy, Miray Ayerdem, and Eray Tüzün · 2023
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 · 2023
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Repobench: Benchmarking repository-level code auto-completion systems
Tianyang Liu, Canwen Xu, and Julian McAuley · 2024
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Starcoder 2 and the stack v2: The next generation
Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, et al · 2024
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An empirical analysis of forgetting in pre-trained models with incremental low-rank updates
Simone Magistri, Joost van de Weijer, Andew D Bagdanov, et al · 2024
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Source code clone detection using unsupervised similarity measures
Jorge Martinez-Gil · 2024
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Sfr-embedding-2: Advanced text embedding with multi-stage training, 2024
Rui Meng*, Ye Liu*, Shafiq Rayhan Joty, Caiming Xiong, Yingbo Zhou, and Semih Yavuz · 2024
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Sfr-embedding-mistral: Enhance text retrieval with transfer learning
Rui Meng, Ye Liu, Shafiq Rayhan Joty, Caiming Xiong, Yingbo Zhou, and Semih Yavuz · 2024
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Nv-retriever: Improving text embedding models with effective hard-negative mining
Gabriel de Souza P Moreira, Radek Osmulski, Mengyao Xu, Ronay Ak, Benedikt Schifferer, and Even Oldridge · 2024
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Sfr-rag: Towards contextually faithful llms
Xuan-Phi Nguyen, Shrey Pandit, Senthil Purushwalkam, Austin Xu, Hailin Chen, Yifei Ming, Zixuan Ke, Silvio Savarese, Caiming Xong, and Shafiq Joty · 2024
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Lost in translation: A study of bugs introduced by large language models while translating code
Rangeet Pan, Ali Reza Ibrahimzada, Rahul Krishna, Divya Sankar, Lambert Pouguem Wassi, Michele Merler, Boris Sobolev, Raju Pavuluri, Saurabh Sinha, and Reyhaneh Jabbarvand · 2024
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Repohyper: Better context retrieval is all you need for repository-level code completion
Huy N Phan, Hoang N Phan, Tien N Nguyen, and Nghi DQ Bui · 2024
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An extractive-and-abstractive framework for source code summarization
Weisong Sun, Chunrong Fang, Yuchen Chen, Quanjun Zhang, Guanhong Tao, Yudu You, Tingxu Han, Yifei Ge, Yuling Hu, Bin Luo, et al · 2024
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Cornstack: High-quality contrastive data for better code ranking
Tarun Suresh, Revanth Gangi Reddy, Yifei Xu, Zach Nussbaum, Andriy Mulyar, Brandon Duderstadt, and Heng Ji · 2024
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Codegemma: Open code models based on gemma
CodeGemma Team · 2024
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Gemma: Open models based on gemini research and technology
Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, et al · 2024
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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 · 2024
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C-pack: Packed resources for general chinese embeddings
Shitao Xiao, Zheng Liu, Peitian Zhang, Niklas Muennighoff, Defu Lian, and Jian-Yun Nie · 2024
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Swe-agent: Agent-computer interfaces enable automated software engineering
John Yang, Carlos E Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, and Ofir Press · 2024
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Code representation learning at scale
Dejiao Zhang, Wasi Uddin Ahmad, Ming Tan, Hantian Ding, Ramesh Nallapati, Dan Roth, Xiaofei Ma, and Bing Xiang · 2024
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Swerank: Software issue localization with code ranking
Revanth Gangi Reddy, Tarun Suresh, JaeHyeok Doo, Ye Liu, Xuan Phi Nguyen, Yingbo Zhou, Semih Yavuz, Caiming Xiong, Heng Ji, and Shafiq Joty · 2025
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