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Finetuning large language models (LLMs) on instructions leads to vast performance improvements on natural language tasks.
SuperGLUE: A stickier benchmark for general-purpose language understanding systems
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman · 1905
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
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Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie · 2005
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Summarizing source code using a neural attention model
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, and Luke Zettlemoyer · 2016
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Antonio Valerio Miceli Barone and Rico Sennrich · 2017
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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Quixbugs: A multi-lingual program repair benchmark set based on the quixey challenge
Derrick Lin, James Koppel, Angela Chen, and Armando Solar-Lezama · 2017
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The limits of automatic summarisation according to rouge
Natalie Schluter · 2017
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A structured review of the validity of bleu
Ehud Reiter · 2018
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Learning to mine aligned code and natural language pairs from stack overflow
Pengcheng Yin, Bowen Deng, Edgar Chen, Bogdan Vasilescu, and Graham Neubig · 2018
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Transformer-xl: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V Le, and Ruslan Salakhutdinov · 2019
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Global relational models of source code
Vincent J Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, and David Bieber · 2019
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Neural code search evaluation dataset
Hongyu Li, Seohyun Kim, and Satish Chandra · 2019
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Fast transformer decoding: One write-head is all you need
Noam M. Shazeer · 2019
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A transformer-based approach for source code summarization
Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang · 2020
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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 · 2020
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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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Gpt-neo: Large scale autoregressive language modeling with mesh-tensorflow
Sid Black, Leo Gao, Phil Wang, Connor Leahy, and Stella Biderman · 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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Nl-augmenter: A framework for task-sensitive natural language augmentation
Kaustubh D Dhole, Varun Gangal, Sebastian Gehrmann, Aadesh Gupta, Zhenhao Li, Saad Mahamood, Abinaya Mahendiran, Simon Mille, Ashish Srivastava, Samson Tan, et al · 2021
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Deepdebug: Fixing python bugs using stack traces, backtranslation, and code skeletons
Dawn Drain, Colin B Clement, Guillermo Serrato, and Neel Sundaresan · 2021
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A framework for few-shot language model evaluation, 2021
Leo Gao, Jonathan Tow, Stella Biderman, Sid Black, Anthony DiPofi, Charles Foster, Laurence Golding, Jeffrey Hsu, Kyle McDonell, Niklas Muennighoff, Jason Phang, Laria Reynolds, Eric Tang, Anish Thite, Ben Wang, Kevin Wang, and Andy Zou · 2021
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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, et al · 2021
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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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Commitbert: Commit message generation using pre-trained programming language model
Tae-Hwan Jung · 2021
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The hateful memes challenge: Competition report
Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Casey A Fitzpatrick, Peter Bull, Greg Lipstein, Tony Nelli, Ron Zhu, et al · 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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Megadiff: A dataset of 600k java source code changes categorized by diff size
Martin Monperrus, Matias Martinez, He Ye, Fernanda Madeiral, Thomas Durieux, and Zhongxing Yu · 2021
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Show your work: Scratchpads for intermediate computation with language models
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, et al · 2021
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True few-shot learning with language models
Ethan Perez, Douwe Kiela, and Kyunghyun Cho · 2021
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Automatic program repair with openai’s codex: Evaluating quixbugs
Julian Aron Prenner and Romain Robbes · 2021
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Train short, test long: Attention with linear biases enables input length extrapolation
Ofir Press, Noah A Smith, and Mike Lewis · 2021
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Do long-range language models actually use long-range context?
Simeng Sun, Kalpesh Krishna, Andrew Mattarella-Micke, and Mohit Iyyer · 2021
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Gpt-j-6b: A 6 billion parameter autoregressive language model, 2021
Ben Wang and Aran Komatsuzaki · 2021
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Break-it-fix-it: Unsupervised learning for program repair
Michihiro Yasunaga and Percy Liang · 2021
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A comprehensive study of automatic program repair on the quixbugs benchmark
He Ye, Matias Martinez, Thomas Durieux, and Martin Monperrus · 2021
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Multi-lingual evaluation of code generation models
Ben Athiwaratkun, Sanjay Krishna Gouda, Zijian Wang, Xiaopeng Li, Yuchen Tian, Ming Tan, Wasi Uddin Ahmad, Shiqi Wang, Qing Sun, Mingyue Shang, et al · 2022
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Training a helpful and harmless assistant with reinforcement learning from human feedback
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al · 2022
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Efficient training of language models to fill in the middle
Mohammad Bavarian, Heewoo Jun, Nikolas A. Tezak, John Schulman, Christine McLeavey, Jerry Tworek, and Mark Chen · 2022
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A framework for the evaluation of code generation models
Loubna Ben Allal, Niklas Muennighoff, Logesh Kumar Umapathi, Ben Lipkin, and Leandro von Werra · 2022
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Gpt-neox-20b: An open-source autoregressive language model
Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, et al · 2022
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Codet: Code generation with generated tests
Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, and Weizhu Chen · 2022
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Pangu-coder: Program synthesis with function-level language modeling
Fenia Christopoulou, Gerasimos Lampouras, Milan Gritta, Guchun Zhang, Yinpeng Guo, Zhongqi Li, Qi Zhang, Meng Xiao, Bo Shen, Lin Li, et al · 2022
Cited alongside, same era.
Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al · 2022
Cited alongside, same era.
Codeexp: Explanatory code document generation
Haotian Cui, Chenglong Wang, Junjie Huang, Jeevana Priya Inala, Todd Mytkowicz, Bo Wang, Jianfeng Gao, and Nan Duan · 2022
Cited alongside, same era.
Flashattention: Fast and memory-efficient exact attention with io-awareness
Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher Ré · 2022
Cited alongside, same era.
Cocomic: Code completion by jointly modeling in-file and cross-file context
Gptscore: Evaluate as you desire
Jinlan Fu, See-Kiong Ng, Zhengbao Jiang, and Pengfei Liu · 2023
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Pal: Program-aided language models
Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig · 2023
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Flacuna: Unleashing the problem solving power of vicuna using flan fine-tuning
Deepanway Ghosal, Yew Ken Chia, Navonil Majumder, and Soujanya Poria · 2023
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Critic: Large language models can self-correct with tool-interactive critiquing
Zhibin Gou, Zhihong Shao, Yeyun Gong, Yelong Shen, Yujiu Yang, Nan Duan, and Weizhu Chen · 2023
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The false promise of imitating proprietary llms
Arnav Gudibande, Eric Wallace, Charlie Snell, Xinyang Geng, Hao Liu, Pieter Abbeel, Sergey Levine, and Dawn Song · 2023
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Yangruibo Ding, Zijian Wang, Wasi Uddin Ahmad, Murali Krishna Ramanathan, Ramesh Nallapati, Parminder Bhatia, Dan Roth, and Bing Xiang · 2022
Cited alongside, same era.
Editeval: An instruction-based benchmark for text improvements
Jane Dwivedi-Yu, Timo Schick, Zhengbao Jiang, Maria Lomeli, Patrick Lewis, Gautier Izacard, Edouard Grave, Sebastian Riedel, and Fabio Petroni · 2022
Cited alongside, same era.
Crystalbleu: precisely and efficiently measuring the similarity of code
Aryaz Eghbali and Michael Pradel · 2022
Cited alongside, same era.
Incoder: A generative model for code infilling and synthesis
Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, and Mike Lewis · 2022
Cited alongside, same era.
Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned
Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, et al · 2022
Cited alongside, same era.
On distribution shift in learning-based bug detectors
Jingxuan He, Luca Beurer-Kellner, and Martin Vechev · 2022
Cited alongside, same era.
Opt-iml: Scaling language model instruction meta learning through the lens of generalization
Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru, Todor Mihaylov, Daniel Simig, Ping Yu, Kurt Shuster, Tianlu Wang, Qing Liu, Punit Singh Koura, Xian Li, Brian O’Horo, Gabriel Pereyra, Jeff Wang, Christopher Dewan, Asli Celikyilmaz, Luke Zettlemoyer, and Ves Stoyanov · 2022
Cited alongside, same era.
Deep Learning-based Code Complexity Prediction
Mingi Jeon, Seung-Yeop Baik, Joonghyuk Hahn, Yo-Sub Han, and Sang-Ki Ko · 2022
Cited alongside, same era.
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Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio César Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, et al · 2023
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Code prompting: a neural symbolic method for complex reasoning in large language models
Yi Hu, Haotong Yang, Zhouchen Lin, and Muhan Zhang · 2023
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Impact of code language models on automated program repair
Nan Jiang, Kevin Liu, Thibaud Lutellier, and Lin Tan · 2023
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Mohammad Abdullah Matin Khan, M Saiful Bari, Xuan Long Do, Weishi Wang, Md Rizwan Parvez, and Shafiq Joty · 2023
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Openassistant conversations–democratizing large language model alignment
Andreas Köpf, Yannic Kilcher, Dimitri von Rütte, Sotiris Anagnostidis, Zhi-Rui Tam, Keith Stevens, Abdullah Barhoum, Nguyen Minh Duc, Oliver Stanley, Richárd Nagyfi, 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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Wizardcoder: Empowering code large language models with evol-instruct
Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, Qingwei Lin, and Daxin Jiang · 2023
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Scaling data-constrained language models
Niklas Muennighoff, Alexander M Rush, Boaz Barak, Teven Le Scao, Aleksandra Piktus, Nouamane Tazi, Sampo Pyysalo, Thomas Wolf, and Colin Raffel · 2023
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Lever: Learning to verify language-to-code generation with execution
Ansong Ni, Srini Iyer, Dragomir Radev, Veselin Stoyanov, Wen-tau Yih, Sida Wang, and Xi Victoria Lin · 2023
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Codegen2: Lessons for training llms on programming and natural languages
Erik Nijkamp, Hiroaki Hayashi, Caiming Xiong, Silvio Savarese, and Yingbo Zhou · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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Measuring the impact of programming language distribution
Gabriel Orlanski, Kefan Xiao, Xavier Garcia, Jeffrey Hui, Joshua Howland, Jonathan Malmaud, Jacob Austin, Rishah Singh, and Michele Catasta · 2023
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Rwkv: Reinventing rnns for the transformer era
Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Huanqi Cao, Xin Cheng, Michael Chung, Matteo Grella, Kranthi Kiran GV, et al · 2023
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On the challenges of using black-box apis for toxicity evaluation in research
Luiza Amador Pozzobon, Beyza Ermis, Patrick Lewis, and Sara Hooker · 2023
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Runbugrun–an executable dataset for automated program repair
Julian Aron Prenner and Romain Robbes · 2023
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Coedit: Text editing by task-specific instruction tuning
Vipul Raheja, Dhruv Kumar, Ryan Koo, and Dongyeop Kang · 2023
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Pangu-coder2: Boosting large language models for code with ranking feedback, 2023
Bo Shen, Jiaxin Zhang, Taihong Chen, Daoguang Zan, Bing Geng, An Fu, Muhan Zeng, Ailun Yu, Jichuan Ji, Jingyang Zhao, Yuenan Guo, and Qianxiang Wang · 2023
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Marta Skreta, Naruki Yoshikawa, Sebastian Arellano-Rubach, Zhi Ji, Lasse Bjørn Kristensen, Kourosh Darvish, Alán Aspuru-Guzik, Florian Shkurti, and Animesh Garg · 2023
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An analysis of the automatic bug fixing performance of chatgpt
Dominik Sobania, Martin Briesch, Carol Hanna, and Justyna Petke · 2023
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Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Creating a coding assistant with starcoder
Lewis Tunstall, Nathan Lambert, Nazneen Rajani, Edward Beeching, Teven Le Scao, Leandro von Werra, Sheon Han, Philipp Schmid, and Alexander Rush · 2023
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Coeditor: Leveraging contextual changes for multi-round code auto-editing
Jiayi Wei, Greg Durrett, and Isil Dillig · 2023
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Style over substance: Evaluation biases for large language models
Minghao Wu and Alham Fikri Aji · 2023
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Conversational automated program repair
Chunqiu Steven Xia and Lingming Zhang · 2023
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Codereval: A benchmark of pragmatic code generation with generative pre-trained models
Hao Yu, Bo Shen, Dezhi Ran, Jiaxin Zhang, Qi Zhang, Yuchi Ma, Guangtai Liang, Ying Li, Tao Xie, and Qianxiang Wang · 2023
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What matters in training a gpt4-style language model with multimodal inputs?
Yan Zeng, Hanbo Zhang, Jiani Zheng, Jiangnan Xia, Guoqiang Wei, Yang Wei, Yuchen Zhang, and Tao Kong · 2023
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Codegeex: A pre-trained model for code generation with multilingual evaluations on humaneval-x
Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Zihan Wang, Lei Shen, Andi Wang, Yang Li, et al · 2023
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Large language models are state-of-the-art evaluators of code generation
Terry Yue Zhuo · 2023
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Kto: Model alignment as prospect theoretic optimization, 2024
Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff, Dan Jurafsky, and Douwe Kiela · 2024
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Olmo: Accelerating the science of language models
Dirk Groeneveld, Iz Beltagy, Pete Walsh, Akshita Bhagia, Rodney Kinney, Oyvind Tafjord, A. Jha, Hamish Ivison, Ian Magnusson, Yizhong Wang, Shane Arora, David Atkinson, Russell Authur, Khyathi Raghavi Chandu, Arman Cohan, Jennifer Dumas, Yanai Elazar, Yuling Gu, Jack Hessel, Tushar Khot, William Merrill, Jacob Daniel Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew E. Peters, Valentina Pyatkin, Abhilasha Ravichander, Dustin Schwenk, Saurabh Shah, Will Smith, Emma Strubell, Nishant Subramani, Mitchell Wortsman, Pradeep Dasigi, Nathan Lambert, Kyle Richardson, Luke Zettlemoyer, Jesse Dodge, Kyle Lo, Luca Soldaini, Noah A. Smith, and Hanna Hajishirzi · 2024
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Generative representational instruction tuning, 2024
Niklas Muennighoff, Hongjin Su, Liang Wang, Nan Yang, Furu Wei, Tao Yu, Amanpreet Singh, and Douwe Kiela · 2024
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2024
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Aya dataset: An open-access collection for multilingual instruction tuning
Shivalika Singh, Freddie Vargus, Daniel Dsouza, Börje F Karlsson, Abinaya Mahendiran, Wei-Yin Ko, Herumb Shandilya, Jay Patel, Deividas Mataciunas, Laura OMahony, et al · 2024
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Dolma: an open corpus of three trillion tokens for language model pretraining research
Luca Soldaini, Rodney Kinney, Akshita Bhagia, Dustin Schwenk, David Atkinson, Russell Authur, Ben Bogin, Khyathi Raghavi Chandu, Jennifer Dumas, Yanai Elazar, Valentin Hofmann, A. Jha, Sachin Kumar, Li Lucy, Xinxi Lyu, Nathan Lambert, Ian Magnusson, Jacob Daniel Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew E. Peters, Abhilasha Ravichander, Kyle Richardson, Zejiang Shen, Emma Strubell, Nishant Subramani, Oyvind Tafjord, Pete Walsh, Luke Zettlemoyer, Noah A. Smith, Hanna Hajishirzi, Iz Beltagy, Dirk Groeneveld, Jesse Dodge, and Kyle Lo · 2024
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Aya model: An instruction finetuned open-access multilingual language model
Ahmet Üstün, Viraat Aryabumi, Zheng-Xin Yong, Wei-Yin Ko, Daniel D’souza, Gbemileke Onilude, Neel Bhandari, Shivalika Singh, Hui-Lee Ooi, Amr Kayid, et al · 2024
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Astraios: Parameter-efficient instruction tuning code large language models
Terry Yue Zhuo, Armel Zebaze, Nitchakarn Suppattarachai, Leandro von Werra, Harm de Vries, Qian Liu, and Niklas Muennighoff · 2024
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