Jianqing Zhu


2024

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AceGPT, Localizing Large Language Models in Arabic
Huang Huang | Fei Yu | Jianqing Zhu | Xuening Sun | Hao Cheng | Song Dingjie | Zhihong Chen | Mosen Alharthi | Bang An | Juncai He | Ziche Liu | Junying Chen | Jianquan Li | Benyou Wang | Lian Zhang | Ruoyu Sun | Xiang Wan | Haizhou Li | Jinchao Xu
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

This paper is devoted to the development of a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models. Significant concerns emerge when addressing cultural sensitivity and local values. To address this, the paper proposes a comprehensive solution that includes further pre-training with Arabic texts, Supervised Fine-Tuning (SFT) utilizing native Arabic instructions, and GPT-4 responses in Arabic, alongside Reinforcement Learning with AI Feedback (RLAIF) employing a reward model attuned to local culture and values. The goal is to cultivate culturally cognizant and value-aligned Arabic LLMs capable of accommodating the diverse, application-specific needs of Arabic-speaking communities. Comprehensive evaluations reveal that the resulting model, dubbed ‘AceGPT’, sets the state-of-the-art standard for open Arabic LLMs across various benchmarks. Codes, data, and models are in https://github.com/FreedomIntelligence/AceGPT.