<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="tong-zhang-2024-codejudge">
    <titleInfo>
        <title>CodeJudge: Evaluating Code Generation with Large Language Models</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Weixi</namePart>
        <namePart type="family">Tong</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Tianyi</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Yaser</namePart>
            <namePart type="family">Al-Onaizan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mohit</namePart>
            <namePart type="family">Bansal</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yun-Nung</namePart>
            <namePart type="family">Chen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Miami, Florida, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Large Language Models (LLMs) have shown promising performance in code generation. However, how to reliably evaluate code generated by LLMs remains an unresolved problem. This paper presents CodeJudge, a code evaluation framework that leverages LLMs to evaluate the semantic correctness of generated code without the need for test cases. We investigate different ways to guide the LLM in performing “slow thinking” to arrive at an in-depth and reliable evaluation. We experimented with four LLMs as evaluators on four code generation datasets and five programming languages. The results show that CodeJudge significantly outperformed existing methods in most settings. Furthermore, compared with a SOTA GPT-3.5-based code evaluation method, CodeJudge achieved better results even when using a much smaller model, Llama-3-8B-Instruct. Our code and datasets are available on GitHub https://github.com/VichyTong/CodeJudge.</abstract>
    <identifier type="citekey">tong-zhang-2024-codejudge</identifier>
    <location>
        <url>https://aclanthology.org/2024.emnlp-main.1118</url>
    </location>
    <part>
        <date>2024-11</date>
        <extent unit="page">
            <start>20032</start>
            <end>20051</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T CodeJudge: Evaluating Code Generation with Large Language Models
%A Tong, Weixi
%A Zhang, Tianyi
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F tong-zhang-2024-codejudge
%X Large Language Models (LLMs) have shown promising performance in code generation. However, how to reliably evaluate code generated by LLMs remains an unresolved problem. This paper presents CodeJudge, a code evaluation framework that leverages LLMs to evaluate the semantic correctness of generated code without the need for test cases. We investigate different ways to guide the LLM in performing “slow thinking” to arrive at an in-depth and reliable evaluation. We experimented with four LLMs as evaluators on four code generation datasets and five programming languages. The results show that CodeJudge significantly outperformed existing methods in most settings. Furthermore, compared with a SOTA GPT-3.5-based code evaluation method, CodeJudge achieved better results even when using a much smaller model, Llama-3-8B-Instruct. Our code and datasets are available on GitHub https://github.com/VichyTong/CodeJudge.
%U https://aclanthology.org/2024.emnlp-main.1118
%P 20032-20051

Markdown (Informal)

[CodeJudge: Evaluating Code Generation with Large Language Models](https://aclanthology.org/2024.emnlp-main.1118) (Tong & Zhang, EMNLP 2024)

• CodeJudge: Evaluating Code Generation with Large Language Models (Tong & Zhang, EMNLP 2024)

ACL

• Weixi Tong and Tianyi Zhang. 2024. CodeJudge: Evaluating Code Generation with Large Language Models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 20032–20051, Miami, Florida, USA. Association for Computational Linguistics.