What is recorded here?
Evaluated GPT-family models fine-tuned on code and introduced HumanEval, a benchmark of Python programming problems tested for functional correctness.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Codex and HumanEval form one paper-level package. Benchmark pass rates do not show reliable software engineering across real repositories.
Read the original work
- Chen and colleagues: Codex code generation and HumanEval — original paper
- Chen and colleagues: Codex code generation and HumanEval — company research page
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Mark Chen; Jerry Tworek; Heewoo Jun; Qiming Yuan; Henrique Ponde de Oliveira Pinto; Jared Kaplan; Harri Edwards; Yuri Burda; Nicholas Joseph; Greg Brockman; Alex Ray; Raul Puri; Gretchen Krueger; Michael Petrov; Heidy Khlaaf; Girish Sastry; Pamela Mishkin; Brooke Chan; Scott Gray; Nick Ryder; Mikhail Pavlov; Alethea Power; Lukasz Kaiser; Mohammad Bavarian; Clemens Winter; Philippe Tillet; Felipe Petroski Such; Dave Cummings; Matthias Plappert; Fotios Chantzis; Elizabeth Barnes; Ariel Herbert-Voss; William Hebgen Guss; Alex Nichol; Alex Paino; Nikolas Tezak; Jie Tang; Igor Babuschkin; Suchir Balaji; Shantanu Jain; William Saunders; Christopher Hesse; Andrew N. Carr; Jan Leike; Josh Achiam; Vedant Misra; Evan Morikawa; Alec Radford; Matthew Knight; Miles Brundage; Mira Murati; Katie Mayer; Peter Welinder; Bob McGrew; Dario Amodei; Sam McCandlish; Ilya Sutskever; Wojciech Zaremba
Complete named paper byline. Human research, source-reported system execution and experimental validation are distinct; individual effort shares are not measured.
Research locations and affiliations
Location not yet established
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English catalogue narrative
Chen and colleagues: Codex code generation and HumanEval
Evaluated GPT-family models fine-tuned on code and introduced HumanEval, a benchmark of Python programming problems tested for functional correctness.
Scientific scope
Codex and HumanEval form one paper-level package. Benchmark pass rates do not show reliable software engineering across real repositories.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Mark Chen; Jerry Tworek; Heewoo Jun; Qiming Yuan; Henrique Ponde de Oliveira Pinto; Jared Kaplan; Harri Edwards; Yuri Burda; Nicholas Joseph; Greg Brockman; Alex Ray; Raul Puri; Gretchen Krueger; Michael Petrov; Heidy Khlaaf; Girish Sastry; Pamela Mishkin; Brooke Chan; Scott Gray; Nick Ryder; Mikhail Pavlov; Alethea Power; Lukasz Kaiser; Mohammad Bavarian; Clemens Winter; Philippe Tillet; Felipe Petroski Such; Dave Cummings; Matthias Plappert; Fotios Chantzis; Elizabeth Barnes; Ariel Herbert-Voss; William Hebgen Guss; Alex Nichol; Alex Paino; Nikolas Tezak; Jie Tang; Igor Babuschkin; Suchir Balaji; Shantanu Jain; William Saunders; Christopher Hesse; Andrew N. Carr; Jan Leike; Josh Achiam; Vedant Misra; Evan Morikawa; Alec Radford; Matthew Knight; Miles Brundage; Mira Murati; Katie Mayer; Peter Welinder; Bob McGrew; Dario Amodei; Sam McCandlish; Ilya Sutskever; Wojciech Zaremba
Complete named paper byline. Human research, source-reported system execution and experimental validation are distinct; individual effort shares are not measured.