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A Systematic Evaluation of Large Language Models of Code
[article]
2022
arXiv
pre-print
Large language models (LMs) of code have recently shown tremendous promise in completing code and synthesizing code from natural language descriptions. However, the current state-of-the-art code LMs (e.g., Codex (Chen et al., 2021)) are not publicly available, leaving many questions about their model and data design decisions. We aim to fill in some of these blanks through a systematic evaluation of the largest existing models: Codex, GPT-J, GPT-Neo, GPT-NeoX-20B, and CodeParrot, across various
arXiv:2202.13169v3
fatcat:y7ukjlndkbe4fnedymmwezvx4m