Fetching the paper…
Reading the bibliography…
Large language models (LLMs) excel at implementing code from functionality descriptions but struggle with algorithmic problems that require not only implementation but also identification of the suitable algorithm.
A deductive approach to program synthesis
Zohar Manna and Richard Waldinger · 1980
Earlier work this paper cites.
From program verification to program synthesis
Saurabh Srivastava, Sumit Gulwani, and Jeffrey S. Foster · 2010
Earlier work this paper cites.
Model-based test oracle generation for automated unit testing of agent systems
Lin Padgham, Zhiyong Zhang, John Thangarajah, and Tim Miller · 2013
Earlier work this paper cites.
Template-based program verification and program synthesis
Saurabh Srivastava, Sumit Gulwani, and Jeffrey Foster · 2013
Earlier work this paper cites.
Flashmeta: A framework for inductive program synthesis
Oleksandr Polozov and Sumit Gulwani · 2015
Earlier work this paper cites.
A theory of formal synthesis via inductive learning
Susmit Jha and Sanjit A Seshia · 2017
Earlier work this paper cites.
Spoc: Search-based pseudocode to code
Sumith Kulal, Panupong Pasupat, Kartik Chandra, Mina Lee, Oded Padon, Alex Aiken, and Percy S Liang · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Earlier work this paper cites.
Automatic test case and test oracle generation based on functional scenarios in formal specifications for conformance testing
Shaoying Liu and Shin Nakajima · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow, March 2021
Sid Black, Leo Gao, Phil Wang, Connor Leahy, and Stella Biderman · 2021
Cited alongside, same era.
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
Cited alongside, same era.
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, and Jacob Steinhardt · 2021
Cited alongside, same era.
Type-directed synthesis of visualizations from natural language queries
Qiaochu Chen, Shankara Pailoor, Celeste Barnaby, Abby Criswell, Chenglong Wang, Greg Durrett, and Işil Dillig · 2022
Cited alongside, same era.
Introduction to algorithms
Thomas H Cormen, Charles E Leiserson, Ronald L Rivest, and Clifford Stein · 2022
Cited alongside, same era.
I speak, you verify: Toward trustworthy neural program synthesis
Darren Key, Wen-Ding Li, and Kevin Ellis · 2022
Later among the works it cites.
Competition-level code generation with alphacode
Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, et al · 2022
Later among the works it cites.
Reading between the lines: Modeling user behavior and costs in ai assisted programming
Hussein Mozannar, Gagan Bansal, Adam Fourney, and Eric Horvitz · 2022
Later among the works it cites.
Natural language to code translation with execution
Freda Shi, Daniel Fried, Marjan Ghazvininejad, Luke Zettlemoyer, and Sida I Wang · 2022
Later among the works it cites.
Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Toga: a neural method for test oracle generation
Elizabeth Dinella, Gabriel Ryan, Todd Mytkowicz, and Shuvendu K Lahiri · 2022
Cited alongside, same era.
Alex Gu, Tamara Mitrovska, Daniela Velez, Jacob Andreas, and Armando Solar-Lezama · 2022
Cited alongside, same era.
Large language models can self-improve
Jiaxin Huang, Shixiang Shane Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, and Jiawei Han · 2022
Cited alongside, same era.
Fault-aware neural code rankers
Jeevana Priya Inala, Chenglong Wang, Mei Yang, Andres Codas, Mark Encarnación, Shuvendu Lahiri, Madanlal Musuvathi, and Jianfeng Gao · 2022
Cited alongside, same era.
Codet: Code generation with generated tests
Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, and Weizhu Chen
Cited in the paper.
Teaching large language models to self-debug
Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou
Cited in the paper.
Planning with large language models for code generation
Shun Zhang, Zhenfang Chen, Yikang Shen, Mingyu Ding, Joshua B. Tenenbaum, and Chuang Gan
Cited in the paper.
Closest in time.
Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al · 2023
Closest in time.
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
Closest in time.
Codegen: An open large language model for code with multi-turn program synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong · 2023
Closest in time.
Learn from mistakes through cooperative interaction with study assistant
Danqing Wang and Lei Li · 2023
Closest in time.