@inproceedings{
liang2024alignment,
title={Alignment at Pre-training! Towards Native Alignment for Arabic {LLM}s},
author={Juhao Liang and Zhenyang Cai and Jianqing Zhu and Huang Huang and Kewei Zong and Bang An and Mosen Alharthi and Juncai He and Lian Zhang and Haizhou Li and Benyou Wang and Jinchao Xu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=woRFmNJiLp}
}
The alignment of large language models (LLMs) is critical for developing effective and safe language models. Traditional approaches focus on aligning models during the instruction tuning or reinforcement learning stages, referred to in this paper as `\textit{post alignment}'. We argue that alignment during the pre-training phase, which we term 'native alignment', warrants investigation. Native alignment aims to prevent unaligned content from the beginning, rather than relying on post-hoc processing. This approach leverages extensively aligned pre-training data to enhance the effectiveness and usability of pre-trained models. Our study specifically explores the application of native alignment in the context of Arabic LLMs. We conduct comprehensive experiments and ablation studies to evaluate the impact of native alignment on model performance and alignment stability. Additionally, we release open-source Arabic LLMs that demonstrate state-of-the-art performance on various benchmarks, providing significant benefits to the Arabic LLM community.
Alignment at Pre-training! Towards Native Alignment for Arabic LLMs
Juhao Liang Zhenyang Cai Jianqing Zhu Huang Huang Kewei Zong Bang An Mosen Alharthi Juncai He Lian Zhang Haizhou Li Benyou Wang Jinchao Xu