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arXiv · 2503.19041

LookAhead Tuning: Safer Language Models via Partial Answer Previews

Kangwei Liu·Mengru Wang·Yujie Luo·Lin Yuan·Mengshu Sun·Lei Liang·Zhiqiang Zhang·Jun Zhou·Bryan Hooi·Shumin Deng
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Abstract

Fine-tuning enables large language models (LLMs) to adapt to specific domains, but often compromises their previously established safety alignment. To mitigate the degradation of model safety during fine-tuning, we introduce LookAhead Tuning, a lightweight and effective data-driven approach that preserves safety during fine-tuning. The method introduces two simple strategies that modify training data by previewing partial answer prefixes, thereby minimizing perturbations to the model's initial token distributions and maintaining its built-in safety mechanisms. Comprehensive experiments demonstrate that LookAhead Tuning effectively maintains model safety without sacrificing robust performance on downstream tasks. Our findings position LookAhead Tuning as a reliable and efficient solution for the safe and effective adaptation of LLMs.

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Kangwei Liu, Mengru Wang, Yujie Luo, Lin Yuan, Mengshu Sun, Lei Liang, Zhiqiang Zhang, Jun Zhou, Bryan Hooi, Shumin Deng. 2025-12-19. LookAhead Tuning: Safer Language Models via Partial Answer Previews. https://arxiv.org/abs/2503.19041

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