Mitigating Gender Bias in Code Large Language Models via Model Editing

In recent years, with the maturation of large language model (LLM) technology and the emergence of high-quality programming code datasets, researchers have become increasingly confident in addressing the challenges of program synthesis automatically. However, since most of the training samples for L...

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Veröffentlicht in:	arXiv.org 2024-10
Hauptverfasser:	Qin, Zhanyue, Wang, Haochuan, Wang, Zecheng, Liu, Deyuan, Fan, Cunhang, Zhao, Lv, Tu, Zhiying, Chu, Dianhui, Sui, Dianbo
Format:	Artikel
Sprache:	eng
Schlagworte:	Bias Datasets Editing Gender Human bias Large language models Parameters Performance evaluation
Online-Zugang:	Volltext
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