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We conduct the first empirical study on using knowledge transfer to improve the generalization ability of large language models (LLMs) in software engineering tasks, which often require LLMs to generalize beyond their training data.
An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers
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Bugbench: Benchmarks for evaluating bug detection tools
Shan Lu, Zhenmin Li, Feng Qin, Lin Tan, Pin Zhou, and Yuanyuan Zhou · 2005
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Semantics-based code search
Steven P Reiss · 2009
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What helps where – and why? semantic relatedness for knowledge transfer
Marcus Rohrbach, Michael Stark, György Szarvas, Iryna Gurevych, and Bernt Schiele · 2010
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Transfer learning
Lisa Torrey and Jude Shavlik · 2010
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2010
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On the naturalness of software
Abram Hindle, Earl T. Barr, Zhendong Su, Mark Gabel, and Premkumar Devanbu · 2012
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Live api documentation
Siddharth Subramanian, Laura Inozemtseva, and Reid Holmes · 2014
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Bugram: bug detection with n-gram language models
Song Wang, Devin Chollak, Dana Movshovitz-Attias, and Lin Tan · 2016
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A survey of transfer learning
Karl Weiss, Taghi M Khoshgoftaar, and DingDing Wang · 2016
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An interpretable knowledge transfer model for knowledge base completion, 2017
Qizhe Xie, Xuezhe Ma, Zihang Dai, and Eduard Hovy · 2017
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A survey of machine learning for big code and naturalness, 2018
Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu, and Charles Sutton · 2018
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Deepbugs: A learning approach to name-based bug detection
Michael Pradel and Koushik Sen · 2018
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Deep code search
Xiaodong Gu, Hongyu Zhang, and Sunghun Kim · 2018
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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
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Combining program analysis and statistical language model for code statement completion
S. Nguyen, Tien Nhut Nguyen, Yi Li, and Shaohua Wang · 2019
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Learning from examples to find fully qualified names of api elements in code snippets
CM Khaled Saifullah, Muhammad Asaduzzaman, and Chanchal K Roy · 2019
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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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Transfer learning in natural language processing
Sebastian Ruder, Matthew E Peters, Swabha Swayamdipta, and Thomas Wolf · 2019
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, T. J. Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeff Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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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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Multi-task learning based pre-trained language model for code completion
Fang Liu, Ge Li, Yunfei Zhao, and Zhi Jin · 2020
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A comprehensive survey on transfer learning
Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He · 2020
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Fine-tuning bert for multi-label sentiment analysis in unbalanced code-switching text
Tiancheng Tang, Xinhuai Tang, and Tianyi Yuan · 2020
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Two-phase multimodal neural network for app categorization using apk resources
Mukund Rungta, Praneet Prabhakar Sherki, Mehak Preet Dhaliwal, Hemant Tiwari, and Vanraj Vala · 2020
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Achieving forgetting prevention and knowledge transfer in continual learning
Zixuan Ke, Bing Liu, Nianzu Ma, Hu Xu, and Lei Shu · 2021
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Does pretraining for summarization require knowledge transfer?, 2021
Kundan Krishna, Jeffrey Bigham, and Zachary C. Lipton · 2021
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Automatic code generation using pre-trained language models, 2021
Prompt-tuned code language model as a neural knowledge base for type inference in statically-typed partial code
Qing Huang, Zhiqiang Yuan, Zhenchang Xing, Xiwei Xu, Liming Zhu, and Qinghua Lu · 2022
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Snr: constraint-based type inference for incomplete java code snippets
Yiwen Dong, Tianxiao Gu, Yongqiang Tian, and Chengnian Sun · 2022
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Qing Huang, Zhiqiang Yuan, Zhenchang Xing, Zhengkang Zuo, Changjing Wang, and Xin Xia · 2022
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Promptchainer: Chaining large language model prompts through visual programming
Tongshuang Wu, Ellen Jiang, Aaron Donsbach, Jeff Gray, Alejandra Molina, Michael Terry, and Carrie J Cai · 2022
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Ai chains: Transparent and controllable human-ai interaction by chaining large language model prompts
Tongshuang Wu, Michael Terry, and Carrie Jun Cai · 2022
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Luis Perez, Lizi Ottens, and Sudharshan Viswanathan · 2021
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Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi · 2021
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What makes good in-context examples for gpt- 3 3 ?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen · 2021
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Unsupervised natural language inference using phl triplet generation
Neeraj Varshney, Pratyay Banerjee, Tejas Gokhale, and Chitta Baral · 2021
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A fine-tuned bert-based transfer learning approach for text classification
Rukhma Qasim, Waqas Haider Bangyal, Mohammed Ali Alqarni, and Abdulwahab Ali Almazroi · 2022
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Bert-based transfer-learning approach for nested named-entity recognition using joint labeling
Ankit Agrawal, Sarsij Tripathi, Manu Vardhan, Vikas Kumar Sihag, Gaurav Choudhary, and Nicola Dragoni · 2022
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Z-code++: A pre-trained language model optimized for abstractive summarization
Pengcheng He, Baolin Peng, Liyang Lu, Song Wang, Jie Mei, Yang Liu, Ruochen Xu, Hany Hassan Awadalla, Yu Shi, Chenguang Zhu, Wayne Xiong, Michael Zeng, Jianfeng Gao, and Xuedong Huang · 2022
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Repair is nearly generation: Multilingual program repair with llms
Harshit Joshi, José Cambronero, Sumit Gulwani, Vu Le, Ivan Radicek, and Gust Verbruggen · 2022
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Large language models are few-shot testers: Exploring llm-based general bug reproduction
Sungmin Kang, Juyeon Yoon, and Shin Yoo · 2022
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Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer · 2022
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Bridging pre-trained models and downstream tasks for source code understanding
Deze Wang, Zhouyang Jia, Shanshan Li, Yue Yu, Yun Xiong, Wei Dong, and Xiangke Liao · 2022
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Cross-domain deep code search with meta learning
Yitian Chai, Hongyu Zhang, Beijun Shen, and Xiaodong Gu · 2022
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On the validity of pre-trained transformers for natural language processing in the software engineering domain
Julian Von der Mosel, Alexander Trautsch, and Steffen Herbold · 2022
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No more fine-tuning? an experimental evaluation of prompt tuning in code intelligence
Chaozheng Wang, Yuanhang Yang, Cuiyun Gao, Yun Peng, Hongyu Zhang, and Michael R Lyu · 2022
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Evaluating how fine-tuning on bimodal data effects code generation
Gabriel Orlanski, Seonhye Yang, and Michael Healy · 2022
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Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
Wenlong Huang, Pieter Abbeel, Deepak Pathak, and Igor Mordatch · 2022
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Meaning without reference in large language models
Steven T Piantasodi and Felix Hill · 2022
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Neural architecture search for effective teacher-student knowledge transfer in language models, 2023
Aashka Trivedi, Takuma Udagawa, Michele Merler, Rameswar Panda, Yousef El-Kurdi, and Bishwaranjan Bhattacharjee · 2023
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Knowledge transfer from pre-trained language models to cif-based speech recognizers via hierarchical distillation, 2023
Minglun Han, Feilong Chen, Jing Shi, Shuang Xu, and Bo Xu · 2023
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Codegeex: A pre-trained model for code generation with multilingual evaluations on humaneval-x, 2023
Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Zihan Wang, Lei Shen, Andi Wang, Yang Li, Teng Su, Zhilin Yang, and Jie Tang · 2023
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Api entity and relation joint extraction from text via dynamic prompt-tuned language model
Qing Huang, Yanbang Sun, Zhenchang Xing, Mingming Yu, Xiwei Xu, and Qinghua Lu · 2023
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Chain-of-thought prompting elicits reasoning in large language models, 2023
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou · 2023
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Pcr-chain: Partial code reuse assisted by hierarchical chaining of prompts on frozen copilot
Qing Huang, Jiahui Zhu, Zhilong Li, Zhenchang Xing, Changjing Wang, and Xiwei Xu · 2023
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