Framework for evaluating code generation ability of large language models

Author:

Yeo Sangyeop1,Ma Yu‐Seung12ORCID,Kim Sang Cheol2ORCID,Jun Hyungkook2,Kim Taeho2ORCID

Affiliation:

1. Division of Artificial Intelligence University of Science and Technology Daejeon Republic of Korea

2. Artificial Intelligence Computing Research Laboratory Electronics and Telecommunications Research Institute Daejeon Republic of Korea

Abstract

AbstractLarge language models (LLMs) have revolutionized various applications in natural language processing and exhibited proficiency in generating programming code. We propose a framework for evaluating the code generation ability of LLMs and introduce a new metric, , which captures the granularity of accuracy according to the pass rate of test cases. The framework is intended to be fully automatic to handle the repetitive work involved in generating prompts, conducting inferences, and executing the generated codes. A preliminary evaluation focusing on the prompt detail, problem publication date, and difficulty level demonstrates the successful integration of our framework with the LeetCode coding platform and highlights the applicability of the metric.

Funder

National Research Council of Science and Technology

Publisher

Wiley

Reference20 articles.

1. M.Chen J.Tworek H.Jun Q.Yuan H. P.deOliveira Pinto J.Kaplan H.Edwards Y.Burda N.Joseph G.Brockman A.Ray R.Puri G.Krueger M.Petrov H.Khlaaf G.Sastry P.Mishkin B.Chan S.Gray N.Ryder M.Pavlov A.Power L.Kaiser M.Bavarian C.Winter P.Tillet F. P.Such D.Cummings M.Plappert F.Chantzis E.Barnes A.Herbert‐Voss W. H.Guss A.Nichol A.Paino N.Tezak J.Tang I.Babuschkin S.Balaji S.Jain W.Saunders C.Hesse A. N.Carr J.Leike J.Achiam V.Misra E.Morikawa A.Radford M.Knight M.Brundage M.Murati K.Mayer P.Welinder B.McGrew D.Amodei S.McCandlish I.Sutskever andW.Zaremba Evaluating large language models trained on code arXiv preprint  2021. DOI10.48550/arXiv.2107.03374

2. Competition-level code generation with AlphaCode

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