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Given that Large Language Models (LLMs) have made significant progress in writing code, can they now be used to autonomously reproduce results from research repositories? Such a capability would be a boon to the research community, helping researchers validate, understand, and extend prior work.
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Wilson L Taylor. 1953 · 1953
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Competition-level code generation with alphacode
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Computational reproducibility of jupyter notebooks from biomedical publications
Sheeba Samuel and Daniel Mietchen. 2022 · 2022
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Shunyu Yao, Howard Chen, John Yang, and Karthik Narasimhan. 2022 · 2022
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ClassEval: A manually-crafted benchmark for evaluating llms on class-level code generation
Xueying Du, Mingwei Liu, Kaixin Wang, Hanlin Wang, Junwei Liu, Yixuan Chen, Jiayi Feng, Chaofeng Sha, Xin Peng, and Yiling Lou. 2023 · 2023
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FixEval: Execution-based evaluation of program fixes for programming problems
Md Mahim Anjum Haque, Wasi Uddin Ahmad, Ismini Lourentzou, and Chris Brown. 2023 · 2023
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DS-1000: A natural and reliable benchmark for data science code generation
Y. Lai, C. Li, Y. Wang, T. Zhang, R. Zhong, L. Zettlemoyer, S. W. Yih, D. Fried, S. Wang, and T. Yu. 2023 · 2023
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API-bank: A comprehensive benchmark for tool-augmented llms
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MLAgentBench: Evaluating language agents on machine learning experimentation
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CrossCodeEval: A diverse and multilingual benchmark for cross-file code completion
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