“Introducing Microsoft 365 Copilot—your Copilot for Work”, Spataro2023

“Introducing Microsoft 365 Copilot—your copilot for work”⁠, Jared Spataro2023-03-26 (GPT-4; similar; bibliography)

“ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics”, Azerbayev Et Al 2023

“ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics”⁠, Zhangir Azerbayev, Bartosz Piotrowski, Hailey Schoelkopf, Edward W. Ayers, Dragomir Radev, Jeremy Avigad2023-02-24 (retrieval AI⁠, knowledge distillation⁠, dataset⁠, math; similar; bibliography)

“CodeBERTScore: Evaluating Code Generation With Pretrained Models of Code”, Zhou Et Al 2023

“CodeBERTScore: Evaluating Code Generation with Pretrained Models of Code”⁠, Shuyan Zhou, Uri Alon, Sumit Agarwal, Graham Neubig2023-02-10 (similar)

“Google Is Asking Employees to Test Potential ChatGPT Competitors, including a Chatbot Called ‘Apprentice Bard’”, Elias2023

“Google is asking employees to test potential ChatGPT competitors, including a chatbot called 'Apprentice Bard'”⁠, Jennifer Elias2023-01-31 (LaMDA⁠, AI scaling/economics; similar; bibliography)

“An Analysis of the Automatic Bug Fixing Performance of ChatGPT”, Sobania Et Al 2023

“An Analysis of the Automatic Bug Fixing Performance of ChatGPT”⁠, Dominik Sobania, Martin Briesch, Carol Hanna, Justyna Petke2023-01-20 (similar; bibliography)

“Connor Leahy on Aliens, Ethics, Economics, Memetics, and Education § GPT-4”, Leahy2023

“Connor Leahy on Aliens, Ethics, Economics, Memetics, and Education § GPT-4”⁠, Connor Leahy2023-01-19 (GPT-4; similar)

“General Availability of Azure OpenAI Service Expands Access to Large, Advanced AI Models With Added Enterprise Benefits”, Boyd2023

“General availability of Azure OpenAI Service expands access to large, advanced AI models with added enterprise benefits”⁠, Eric Boyd2023-01-16 (AI scaling/economics; similar; bibliography)

“SantaCoder: Don’t Reach for the Stars!”, Allal Et Al 2023

“SantaCoder: don’t reach for the stars!”⁠, Loubna Ben Allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Munoz Ferrandis2023-01-09 (similar)

“TrojanPuzzle: Covertly Poisoning Code-Suggestion Models”, Aghakhani Et Al 2023

“TrojanPuzzle: Covertly Poisoning Code-Suggestion Models”⁠, Hojjat Aghakhani, Wei Dai, Andre Manoel, Xavier Fernandes, Anant Kharkar, Christopher Kruegel, Giovanni Vigna2023-01-06 (neural net/adversarial; similar)

“ERNIE-Code: Beyond English-Centric Cross-lingual Pretraining for Programming Languages”, Chai Et Al 2022

“ERNIE-Code: Beyond English-Centric Cross-lingual Pretraining for Programming Languages”⁠, Yekun Chai, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu2022-12-13 (T5 Transformer⁠, AI scaling; similar)

“The Stack: 3 TB of Permissively Licensed Source Code”, Kocetkov Et Al 2022

“The Stack: 3 TB of permissively licensed source code”⁠, Denis Kocetkov, Raymond Li, Loubna Ben Allal, Jia Li, Chenghao Mou, Carlos Muñoz Ferrandis, Yacine Jernite2022-11-20 (ML dataset⁠, Python; similar; bibliography)

“PAL: Program-aided Language Models”, Gao Et Al 2022

“PAL: Program-aided Language Models”⁠, Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, Graham Neubig2022-11-18 (inner monologue (AI); similar)

“Programming Possibility: Kevin Scott on AI’s Impact on Cognitive Work”, Hoffman & Scott2022

“Programming Possibility: Kevin Scott on AI’s Impact on Cognitive Work”⁠, Reid Hoffman, Kevin Scott2022-10-18 (similar; bibliography)

“Challenging BIG-Bench Tasks (BBH) and Whether Chain-of-Thought Can Solve Them”, Suzgun Et Al 2022

“Challenging BIG-Bench Tasks (BBH) and Whether Chain-of-Thought Can Solve Them”⁠, Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery2022-10-17 (inner monologue (AI)⁠, PaLM⁠, emergence (AI); similar; bibliography)

“Vote-K: Selective Annotation Makes Language Models Better Few-Shot Learners”, Su Et Al 2022

“Vote-K: 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 Ostendorf2022-09-05 (retrieval AI⁠, RL exploration; similar; bibliography)

“Repair Is Nearly Generation: Multilingual Program Repair With LLMs”, Joshi Et Al 2022

“Repair Is Nearly Generation: Multilingual Program Repair with LLMs”⁠, Harshit Joshi, José Cambronero, Sumit Gulwani, Vu Le, Ivan Radicek, Gust Verbruggen2022-08-24 (similar)

“Language Models Can Teach Themselves to Program Better”, Haluptzok Et Al 2022

“Language Models Can Teach Themselves to Program Better”⁠, Patrick Haluptzok, Matthew Bowers, Adam Tauman Kalai2022-07-29 (ML dataset⁠, inner monologue (AI); similar)

“Efficient Training of Language Models to Fill in the Middle”, Bavarian Et Al 2022

“Efficient Training of Language Models to Fill in the Middle”⁠, Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, John Schulman, Christine McLeavey, Jerry Tworek, Mark Chen2022-07-28 (similar)

“PanGu-Coder: Program Synthesis With Function-Level Language Modeling”, Christopoulou Et Al 2022

“PanGu-Coder: Program Synthesis with Function-Level Language Modeling”⁠, Fenia Christopoulou, Gerasimos Lampouras, Milan Gritta, Guchun Zhang, Yinpeng Guo, Zhongqi Li, Qi Zhang2022-07-22 (similar)

“CodeT: Code Generation With Generated Tests”, Chen Et Al 2022

“CodeT: Code Generation with Generated Tests”⁠, Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, Weizhu Chen2022-07-21 (inner monologue (AI); similar)

“Can Large Language Models Reason about Medical Questions?”, Liévin Et Al 2022

“Can large language models reason about medical questions?”⁠, Valentin Liévin, Christoffer Egeberg Hother, Ole Winther2022-07-17 (GPT/calibration⁠, inner monologue (AI)⁠, GPT non-fiction⁠, biology; similar; bibliography)

“Craft an Iron Sword: Dynamically Generating Interactive Game Characters by Prompting Large Language Models Tuned on Code”, Volum Et Al 2022

“Craft an Iron Sword: Dynamically Generating Interactive Game Characters by Prompting Large Language Models Tuned on Code”⁠, Ryan Volum, Sudha Rao, Michael Xu, Gabriel A. DesGarennes, Chris Brockett, Benjamin Van Durme, Olivia Deng2022-07-05 (similar)

“Code Translation With Compiler Representations”, Szafraniec Et Al 2022

“Code Translation with Compiler Representations”⁠, Marc Szafraniec, Baptiste Roziere, Hugh Leather Francois Charton, Patrick Labatut, Gabriel Synnaeve2022-06-30 (similar)

“Repository-Level Prompt Generation for Large Language Models of Code”, Shrivastava Et Al 2022

“Repository-Level Prompt Generation for Large Language Models of Code”⁠, Disha Shrivastava, Hugo Larochelle, Daniel Tarlow2022-06-26 (similar)

“Productivity Assessment of Neural Code Completion”, Ziegler Et Al 2022

“Productivity Assessment of Neural Code Completion”⁠, Albert Ziegler, Eirini Kalliamvakou, Shawn Simister, Ganesh Sittampalam, Alice Li, Andrew Rice, Devon Rifkin2022-05-13 (similar; bibliography)

“End-to-end Symbolic Regression With Transformers”, Kamienny Et Al 2022

“End-to-end symbolic regression with transformers”⁠, Pierre-Alexandre Kamienny, Stéphane d’Ascoli, Guillaume Lample, François Charton2022-04-22 (math; similar)

“PaLM: Scaling Language Modeling With Pathways”, Chowdhery Et Al 2022

“PaLM: Scaling Language Modeling with Pathways”⁠, Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham2022-04-05 (LaMDA⁠, PaLM⁠, emergence (AI)⁠, math; similar; bibliography)

“A Conversational Paradigm for Program Synthesis”, Nijkamp Et Al 2022

“A Conversational Paradigm for Program Synthesis”⁠, Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong2022-03-25 (inner monologue (AI)⁠, AI scaling; similar)

“Evaluating the Text-to-SQL Capabilities of Large Language Models”, Rajkumar Et Al 2022

“Evaluating the Text-to-SQL Capabilities of Large Language Models”⁠, Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau2022-03-15 (similar)

“Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models”, Vaithilingam Et Al 2022

“Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models”⁠, Priyan Vaithilingam, Tianyi Zhang, Elena Glassman2022-03-06 (design; similar; bibliography)

“PolyCoder: A Systematic Evaluation of Large Language Models of Code”, Xu Et Al 2022

“PolyCoder: A Systematic Evaluation of Large Language Models of Code”⁠, Frank F. Xu, Uri Alon, Graham Neubig, Vincent J. Hellendoorn2022-02-26 (similar)

“Pop Quiz! Can a Large Language Model Help With Reverse Engineering?”, Pearce Et Al 2022

“Pop Quiz! Can a Large Language Model Help With Reverse Engineering?”⁠, Hammond Pearce, Benjamin Tan, Prashanth Krishnamurthy, Farshad Khorrami, Ramesh Karri, Brendan Dolan-Gavitt2022-02-02 (CS/security; similar)

“Text and Code Embeddings by Contrastive Pre-Training”, Neelakantan Et Al 2022

“Text and Code Embeddings by Contrastive Pre-Training”⁠, Arvind Neelakantan, Tao Xu, Raul Puri, Alec Radford, Jesse Michael Han, Jerry Tworek, Qiming Yuan, Nikolas Tezak2022-01-24 (AI scaling; similar; bibliography)

“Neural Language Models Are Effective Plagiarists”, Biderman & Raff2022

“Neural Language Models are Effective Plagiarists”⁠, Stella Biderman, Edward Raff2022-01-19 (similar)

“Deep Symbolic Regression for Recurrent Sequences”, D’Ascoli Et Al 2022

“Deep Symbolic Regression for Recurrent Sequences”⁠, Stéphane d’Ascoli, Pierre-Alexandre Kamienny, Guillaume Lample, François Charton2022-01-12 (math; similar)

“Discovering the Syntax and Strategies of Natural Language Programming With Generative Language Models”, Jiang Et Al 2022

“Discovering the Syntax and Strategies of Natural Language Programming with Generative Language Models”⁠, Ellen Jiang, Edwin Toh, Alejandra Molina, Kristen Olson, Claire Kayacik, Aaron Donsbach, Carrie J. Cai2022-01-06 (LaMDA; backlinks; similar)

“A Neural Network Solves and Generates Mathematics Problems by Program Synthesis: Calculus, Differential Equations, Linear Algebra, and More”, Drori Et Al 2021

“A Neural Network Solves and Generates Mathematics Problems by Program Synthesis: Calculus, Differential Equations, Linear Algebra, and More”⁠, Iddo Drori, Sunny Tran, Roman Wang, Newman Cheng, Kevin Liu, Leonard Tang, Elizabeth Ke, Nikhil Singh2021-12-31 (inner monologue (AI)⁠, math; similar)

“WebGPT: Improving the Factual Accuracy of Language Models through Web Browsing”, Hilton Et Al 2021

“WebGPT: Improving the factual accuracy of language models through web browsing”⁠, Jacob Hilton, Suchir Balaji, Reiichiro Nakano, John Schulman2021-12-16 (retrieval AI⁠, inner monologue (AI)⁠, preference learning⁠, RL scaling; similar; bibliography)

“WebGPT: Browser-assisted Question-answering With Human Feedback”, Nakano Et Al 2021

“WebGPT: Browser-assisted question-answering with human feedback”⁠, Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu Long Ouyang, Christina Kim, Christopher Hesse2021-12-16 (ML dataset⁠, retrieval AI⁠, preference learning⁠, RL scaling; similar; bibliography)

“Few-Shot Semantic Parsing With Language Models Trained On Code”, Shin & Durme2021

“Few-Shot Semantic Parsing with Language Models Trained On Code”⁠, Richard Shin, Benjamin Van Durme2021-12-16 (similar)

“Scaling Language Models: Methods, Analysis & Insights from Training Gopher”, Rae Et Al 2021

“Scaling Language Models: Methods, Analysis & Insights from Training Gopher”⁠, Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides2021-12-08 (GPT poetry⁠, T5 Transformer⁠, emergence (AI)⁠, math⁠, AI safety; similar; bibliography)

“Jigsaw: Large Language Models Meet Program Synthesis”, Jain Et Al 2021

“Jigsaw: Large Language Models meet Program Synthesis”⁠, Naman Jain, Skanda Vaidyanath, Arun Iyer, Nagarajan Natarajan, Suresh Parthasarathy, Sriram Rajamani2021-12-06 (similar)

“Can Pre-trained Language Models Be Used to Resolve Textual and Semantic Merge Conflicts?”, Zhang Et Al 2021

“Can Pre-trained Language Models be Used to Resolve Textual and Semantic Merge Conflicts?”⁠, Jialu Zhang, Todd Mytkowicz, Mike Kaufman, Ruzica Piskac, Shuvendu K. Lahiri2021-11-23 (AI scaling; similar; bibliography)

“Solving Probability and Statistics Problems by Program Synthesis”, Tang Et Al 2021

“Solving Probability and Statistics Problems by Program Synthesis”⁠, Leonard Tang, Elizabeth Ke, Nikhil Singh, Nakul Verma, Iddo Drori2021-11-16 (ML dataset⁠, AI scaling; backlinks; similar; bibliography)

“Solving Linear Algebra by Program Synthesis”, Drori & Verma2021

“Solving Linear Algebra by Program Synthesis”⁠, Iddo Drori, Nakul Verma2021-11-16 (AI scaling; backlinks; similar)

“Automatic Program Repair With OpenAI’s Codex: Evaluating QuixBugs”, Prenner & Robbes2021

“Automatic Program Repair with OpenAI’s Codex: Evaluating QuixBugs”⁠, Julian Aron Prenner, Romain Robbes2021-11-06 (backlinks; similar)

“GenLine and GenForm: Two Tools for Interacting With Generative Language Models in a Code Editor”, Jiang Et Al 2021

“GenLine and GenForm: Two Tools for Interacting with Generative Language Models in a Code Editor”⁠, Ellen Jiang, Edwin Toh, Alejandra Molina, Aaron Donsbach, Carrie Cai, Michael Terry2021-09-07 (LaMDA; similar; bibliography)

“An Empirical Cybersecurity Evaluation of GitHub Copilot’s Code Contributions”, Pearce Et Al 2021

“An Empirical Cybersecurity Evaluation of GitHub Copilot’s Code Contributions”⁠, Hammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt, Ramesh Karri2021-08-20 (AI safety; similar)

“Learning C to X86 Translation: An Experiment in Neural Compilation”, Armengol-Estapé & O’Boyle2021

“Learning C to x86 Translation: An Experiment in Neural Compilation”⁠, Jordi Armengol-Estapé, Michael F. P. O’Boyle2021-08-17 (similar)

“Program Synthesis With Large Language Models”, Austin Et Al 2021

“Program Synthesis with Large Language Models”⁠, Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang2021-08-16 (NN sampling⁠, inner monologue (AI)⁠, LaMDA⁠, emergence (AI); similar)

“TAPEX: Table Pre-training via Learning a Neural SQL Executor”, Liu Et Al 2021

“TAPEX: Table Pre-training via Learning a Neural SQL Executor”⁠, Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou2021-07-16 (tabular ML; similar)

“Evaluating Large Language Models Trained on Code”, Chen Et Al 2021

“Evaluating Large Language Models Trained on Code”⁠, Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards2021-07-07 (AI safety⁠, RL scaling; similar)

“Research Recitation: A First Look at Rote Learning in GitHub Copilot Suggestions”, Ziegler2021

“Research recitation: A first look at rote learning in GitHub Copilot suggestions”⁠, Albert Ziegler2021-07 (similar)

“Microsoft and OpenAI Have a New A.I. Tool That Will Give Coding Suggestions to Software Developers”, Novet2021

“Microsoft and OpenAI have a new A.I. tool that will give coding suggestions to software developers”⁠, Jordan Novet2021-06-29 (similar)

“SymbolicGPT: A Generative Transformer Model for Symbolic Regression”, Valipour Et Al 2021

“SymbolicGPT: A Generative Transformer Model for Symbolic Regression”⁠, Mojtaba Valipour, Bowen You, Maysum Panju, Ali Ghodsi2021-06-27 (math; backlinks; similar)

“Measuring Coding Challenge Competence With APPS”, Hendrycks Et Al 2021

“Measuring Coding Challenge Competence With APPS”⁠, Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik2021-05-20 (backlinks; similar)

“Improving Code Autocompletion With Transfer Learning”, Zhou Et Al 2021

“Improving Code Autocompletion with Transfer Learning”⁠, Wen Zhou, Seohyun Kim, Vijayaraghavan Murali, Gareth Ari Aye2021-05-12 (similar)

“Learning Autocompletion from Real-World Datasets”, Aye Et Al 2020

“Learning Autocompletion from Real-World Datasets”⁠, Gareth Ari Aye, Seohyun Kim, Hongyu Li2020-11-09 (similar)

“GraphCodeBERT: Pre-training Code Representations With Data Flow”, Guo Et Al 2020

“GraphCodeBERT: Pre-training Code Representations with Data Flow”⁠, Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy2020-09-17 (similar)

“CoCoNuT: Combining Context-Aware Neural Translation Models Using Ensemble for Program Repair”, Lutellier Et Al 2020

“CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair”⁠, Thibaud Lutellier, Hung Viet Pham, Lawrence Pang, Yitong Li, Moshi Wei, Lin Tan2020-07-01 (CNN; backlinks; similar)

“TransCoder: Unsupervised Translation of Programming Languages”, Lachaux Et Al 2020

“TransCoder: Unsupervised Translation of Programming Languages”⁠, Marie-Anne Lachaux, Baptiste Roziere, Lowik Chanussot, Guillaume Lample2020-06-05 (similar)

“GPT-3 Random Sample Dump: JavaScript Tutorial”, GPT-3 2020

“GPT-3 random sample dump: JavaScript tutorial”⁠, GPT-32020-05-28 (similar)

“IntelliCode Compose: Code Generation Using Transformer”, Svyatkovskiy Et Al 2020

“IntelliCode Compose: Code Generation Using Transformer”⁠, Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, Neel Sundaresan2020-05-16 (similar)

“Deep Learning for Symbolic Mathematics”, Lample & Charton2019

“Deep Learning for Symbolic Mathematics”⁠, Guillaume Lample, François Charton2019-12-02 (math; similar)

“CodeSearchNet Challenge: Evaluating the State of Semantic Code Search”, Husain Et Al 2019

“CodeSearchNet Challenge: Evaluating the State of Semantic Code Search”⁠, Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, Marc Brockschmidt2019-09-20 (similar)

“BERTScore: Evaluating Text Generation With BERT”, Zhang Et Al 2019

“BERTScore: Evaluating Text Generation with BERT”⁠, Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, Yoav Artzi2019-04-21 (backlinks; similar)

“Seq2SQL: Generating Structured Queries from Natural Language Using Reinforcement Learning”, Zhong Et Al 2017

“Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning”⁠, Victor Zhong, Caiming Xiong, Richard Socher2017-08-31 (retrieval AI⁠, model-free RL⁠, Wikipedia; backlinks; similar)

“Learning to Superoptimize Programs”, Bunel Et Al 2017

“Learning to superoptimize programs”⁠, Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli2017-02-23 (CS/algorithm⁠, meta-learning; similar)

“DeepCoder: Learning to Write Programs”, Balog Et Al 2016

“DeepCoder: Learning to Write Programs”⁠, Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, Daniel Tarlow2016-11-07 (RNN; similar)

“OpenAI API Alchemy: Smart Formatting and Code Creation”

“Transformer-VAE for Program Synthesis”

Wikipedia

1. GitHub Copilot

2. OpenAI Codex