Answer-then-Edit: Reasoning Skeleton Editing for Anti-Distillation with Preserved Utility
Proprietary large language models (LLMs) entail substantial intellectual and financial investment, making them valuable intellectual property (IP). However, even when deployed via black-box APIs, these models remain vulnerable to unauthorized knowledge distillation, which allows adversaries to cheaply extract and replicate model capabilities. To address this issue, anti-distillation (AD) has been proposed to generate defensive outputs that hinder distillation effectiveness, overcoming the limitation of watermarking-based approaches that rely on post-hoc verification. However, existing AD methods based on internal model perturbations struggle to balance anti-distillability and utility (e.g...
| Authors | Fan Li, Mengting Pan, Sijia Xu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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