@inproceedings{he-etal-2024-cocost,
title = "{C}o{C}o{ST}: Automatic Complex Code Generation with Online Searching and Correctness Testing",
author = "He, Xinyi and
Zou, Jiaru and
Lin, Yun and
Zhou, Mengyu and
Han, Shi and
Yuan, Zejian and
Zhang, Dongmei",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.1082",
pages = "19433--19451",
abstract = "Large Language Models have revolutionized code generation ability by converting natural language descriptions into executable code. However, generating complex code within real-world scenarios remains challenging due to intricate structures, subtle bugs, understanding of advanced data types, and lack of supplementary contents. To address these challenges, we introduce the CoCoST framework, which enhances complex code generation by online searching for more information with planned queries and correctness testing for code refinement. Moreover, CoCoST serializes the complex inputs and outputs to improve comprehension and generates test cases to ensure the adaptability for real-world applications. CoCoST is validated through rigorous experiments on the DS-1000 and ClassEval datasets. Experimental results show that CoCoST substantially improves the quality of complex code generation, highlighting its potential to enhance the practicality of LLMs in generating complex code.",
}
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<abstract>Large Language Models have revolutionized code generation ability by converting natural language descriptions into executable code. However, generating complex code within real-world scenarios remains challenging due to intricate structures, subtle bugs, understanding of advanced data types, and lack of supplementary contents. To address these challenges, we introduce the CoCoST framework, which enhances complex code generation by online searching for more information with planned queries and correctness testing for code refinement. Moreover, CoCoST serializes the complex inputs and outputs to improve comprehension and generates test cases to ensure the adaptability for real-world applications. CoCoST is validated through rigorous experiments on the DS-1000 and ClassEval datasets. Experimental results show that CoCoST substantially improves the quality of complex code generation, highlighting its potential to enhance the practicality of LLMs in generating complex code.</abstract>
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%0 Conference Proceedings
%T CoCoST: Automatic Complex Code Generation with Online Searching and Correctness Testing
%A He, Xinyi
%A Zou, Jiaru
%A Lin, Yun
%A Zhou, Mengyu
%A Han, Shi
%A Yuan, Zejian
%A Zhang, Dongmei
%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 he-etal-2024-cocost
%X Large Language Models have revolutionized code generation ability by converting natural language descriptions into executable code. However, generating complex code within real-world scenarios remains challenging due to intricate structures, subtle bugs, understanding of advanced data types, and lack of supplementary contents. To address these challenges, we introduce the CoCoST framework, which enhances complex code generation by online searching for more information with planned queries and correctness testing for code refinement. Moreover, CoCoST serializes the complex inputs and outputs to improve comprehension and generates test cases to ensure the adaptability for real-world applications. CoCoST is validated through rigorous experiments on the DS-1000 and ClassEval datasets. Experimental results show that CoCoST substantially improves the quality of complex code generation, highlighting its potential to enhance the practicality of LLMs in generating complex code.
%U https://aclanthology.org/2024.emnlp-main.1082
%P 19433-19451
Markdown (Informal)
[CoCoST: Automatic Complex Code Generation with Online Searching and Correctness Testing](https://aclanthology.org/2024.emnlp-main.1082) (He et al., EMNLP 2024)
ACL
- Xinyi He, Jiaru Zou, Yun Lin, Mengyu Zhou, Shi Han, Zejian Yuan, and Dongmei Zhang. 2024. CoCoST: Automatic Complex Code Generation with Online Searching and Correctness Testing. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 19433–19451, Miami, Florida, USA. Association for Computational Linguistics.