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Recent work targeting large language models (LLMs) for code generation demonstrated that increasing the amount of training data through synthetic code generation often leads to exceptional performance.
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Scikit-learn: Machine learning in Python
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A systematic review of fuzzing techniques
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Estimating training data influence by tracing gradient descent
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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Trivial or impossible–dichotomous data difficulty masks model differences (on imagenet and beyond)
Kristof Meding, Luca M Schulze Buschoff, Robert Geirhos, and Felix A Wichmann · 2021
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Toward an effective black-box adversarial attack on functional javascript malware against commercial anti-virus
Yun-Da Tsai, ChengKuan Chen, and Shou-De Lin · 2021
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le · 2021
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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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Program synthesis with large language models
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al · 2021
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Self-instruct: Aligning language models with self-generated instructions
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A Smith, Daniel Khashabi, and Hannaneh Hajishirzi · 2022
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Selective annotation makes language models better few-shot learners
Hongjin Su, Jungo Kasai, Chen Henry Wu, Weijia Shi, Tianlu Wang, Jiayi Xin, Rui Zhang, Mari Ostendorf, Luke Zettlemoyer, Noah A Smith, et al · 2022
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Fast online inference for nonlinear contextual bandit based on generative adversarial network
Yun Da Tsai and Shou De Lin · 2022
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Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, and Jianfeng Gao · 2023
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Ming Li, Yong Zhang, Zhitao Li, Jiuhai Chen, Lichang Chen, Ning Cheng, Jianzong Wang, Tianyi Zhou, and Jing Xiao · 2023
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Efficient memory management for large language model serving with pagedattention
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica · 2023
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When scaling meets llm finetuning: The effect of data, model and finetuning method
Biao Zhang, Zhongtao Liu, Colin Cherry, and Orhan Firat · 2024
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Wizardcoder: Empowering code large language models with evol-instruct
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Beyond neural scaling laws: beating power law scaling via data pruning
Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, and Ari Morcos · 2022
Cited alongside, same era.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Code alpaca: An instruction-following llama model for code generation
Sahil Chaudhary · 2023
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Magicoder: Source code is all you need
Yuxiang Wei, Zhe Wang, Jiawei Liu, Yifeng Ding, and Lingming Zhang · 2023
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Lima: Less is more for alignment, 2023
Chunting Zhou, Pengfei Liu, Puxin Xu, Srini Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, Susan Zhang, Gargi Ghosh, Mike Lewis, Luke Zettlemoyer, and Omer Levy · 2023
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On the effectiveness of parameter-efficient fine-tuning
Zihao Fu, Haoran Yang, Anthony Man-Cho So, Wai Lam, Lidong Bing, and Nigel Collier · 2023
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Active prompting with chain-of-thought for large language models
Shizhe Diao, Pengcheng Wang, Yong Lin, and Tong Zhang · 2023
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Too large; data reduction for vision-language pre-training
Alex Jinpeng Wang, Kevin Qinghong Lin, David Junhao Zhang, Stan Weixian Lei, and Mike Zheng Shou · 2023
Cited alongside, same era.
Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, Qingwei Lin, and Daxin Jiang · 2024
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Fineweb, April 2024
Guilherme Penedo, Hynek Kydlíček, Leandro von Werra, and Thomas Wolf · 2024
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Handling concept drift in non-stationary bandit through predicting future rewards
Yun-Da Tsai and Shou-De Lin · 2024
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Less: Selecting influential data for targeted instruction tuning
Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, Sanjeev Arora, and Danqi Chen · 2024
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Alpagasus: Training a better alpaca with fewer data, 2024
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A study in dataset pruning for image super-resolution
Brian B Moser, Federico Raue, and Andreas Dengel · 2024
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Starcoder 2 and the stack v2: The next generation
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Scaling instruction-finetuned language models
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Superfiltering: Weak-to-strong data filtering for fast instruction-tuning
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Atharva Naik · 2024
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Deepseek-coder: When the large language model meets programming – the rise of code intelligence, 2024
Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Y. Wu, Y. K. Li, Fuli Luo, Yingfei Xiong, and Wenfeng Liang · 2024
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