Personality-Guided Code Generation Using Large Language Models
Yaoqi Guo, Zhenpeng Chen, Jie M. Zhang, Yang Liu, Yun Ma
Abstract
Code generation, the automatic creation of source code from natural language descriptions, has garnered significant attention due to its potential to streamline software development. Inspired by research that links task-personality alignment with improved development outcomes, we conduct an empirical study on personality-guided code generation using large language models (LLMs). Specifically, we investigate how emulating personality traits appropriate to the coding tasks affects LLM performance. We extensively evaluate this approach using seven widely adopted LLMs across four representative datasets. Our results show that personality guidance significantly enhances code generation accuracy, with improved pass rates in 23 out of 28 LLM-dataset combinations. Notably, in 11 cases, the improvement exceeds 5%, and in 5 instances, it surpasses 10%, with the highest gain reaching 12.9%. Additionally, personality guidance can be easily integrated with other prompting strategies to further boost performance.
BibTeX
@inproceedings{guo-etal-2025-personality,
title = "Personality-Guided Code Generation Using Large Language Models",
author = "Guo, Yaoqi and
Chen, Zhenpeng and
Zhang, Jie M. and
Liu, Yang and
Ma, Yun",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.54/",
doi = "10.18653/v1/2025.acl-long.54",
pages = "1068--1080",
ISBN = "979-8-89176-251-0"
}