title: LookAhead Tuning: Safer Language Models via Partial Answer Previews
summary: Fine-tuning enables large language models (LLMs) to adapt to specific
  domains, but often undermines their previously established safety alignment. To
  mitigate the degradation of model safety during fine-tuning, we introduce
  LookAhead Tuning, which comprises two simple, low-resource, and effective
  data-driven methods that modify training data by previewing partial answer
  prefixes. Both methods aim to preserve the model's inherent safety mechanisms
  by minimizing perturbations to initial token distributions. 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. Code is released at
  https://github.com/zjunlp/LookAheadTuning.
paper_authors._id: 67e35da0b1b97cc3392024b1
paper_authors.name: Kangwei Liu
paper_authors._id: 67e35da0b1b97cc3392024b2
paper_authors.name: Mengru Wang
paper_authors._id: 67e35da0b1b97cc3392024b3
paper_authors.name: Yujie Luo
paper_authors._id: 67e35da0b1b97cc3392024b4
paper_authors.name: Lin Yuan
paper_authors._id: 67e35da0b1b97cc3392024b5
paper_authors.name: Mengshu Sun
paper_authors._id: 67e35da0b1b97cc3392024b6
paper_authors.name: Ningyu Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T12:27:22.449Z
paper_authors._id: 620b3bbb0668e435407c8d0a
paper_authors.avatarUrl: /avatars/e0fccbb2577d76088e09f054c35cffbc.svg
paper_authors.fullname: Ningyu Zhang
paper_authors.type: user
paper_authors.user: Ningyu
paper_authors._id: 67e35da0b1b97cc3392024b7
paper_authors.name: Lei Liang
paper_authors._id: 67e35da0b1b97cc3392024b8
paper_authors.name: Zhiqiang Zhang
paper_authors._id: 67e35da0b1b97cc3392024b9
paper_authors.name: Jun Zhou
paper_authors._id: 67e35da0b1b97cc3392024ba
paper_authors.name: Huajun Chen
paper_title: LookAhead Tuning: Safer Language Models via Partial Answer Previews
paper_summary: Fine-tuning enables large language models (LLMs) to adapt to specific
  domains, but often undermines their previously established safety alignment. To
  mitigate the degradation of model safety during fine-tuning, we introduce
  LookAhead Tuning, which comprises two simple, low-resource, and effective
  data-driven methods that modify training data by previewing partial answer
  prefixes. Both methods aim to preserve the model's inherent safety mechanisms
  by minimizing perturbations to initial token distributions. 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. Code is released at
  https://github.com/zjunlp/LookAheadTuning.
paper_ai_summary: LookAhead Tuning, a data-driven approach, enhances the safety of large language models during fine-tuning by minimizing token distribution changes, while maintaining robust performance.
paper_ai_keywords: LookAhead Tuning large language models (LLMs) safety alignment fine-tuning token distribution safety mechanisms