Protecting Language Models Against Unauthorized Distillation through Trace Rewriting
Summary: arXiv:2602.15143v2 Announce Type: replace
Abstract
Knowledge distillation is a widely adopted technique for transferring capabilities from large language models (LLMs) to smaller, more efficient student models. However, unauthorized use of knowledge distillation takes unfair advantage of the considerable effort and cost put into developing frontier models. In this article, we investigate methods for modifying teacher-generated reasoning traces to achieve two objectives that deter unauthorized distillation:
- Anti-distillation: Degrading the training usefulness of query responses.
- API watermarking: Embedding verifiable signatures in student models.
Introduction
As the field of artificial intelligence continues to advance, the capabilities of large language models have become increasingly sophisticated. However, this sophistication also comes with the risk of unauthorized knowledge distillation, where malicious entities could siphon off valuable insights from these models without permission. To address this issue, we propose a set of innovative techniques aimed at protecting the intellectual property embedded within these models.
Methodology
Our research introduces several approaches for dynamically rewriting a teacher’s reasoning outputs. The primary goals are to maintain answer correctness and semantic coherence while implementing the protective measures. Specifically, we explore:
- LLM-based rewriting: Utilizing the inherent capabilities of language models to alter reasoning outputs without compromising their quality.
- Gradient-based techniques: Applying mathematical gradients to modify the outputs in a way that makes unauthorized distillation more challenging.
Results
Our experiments reveal that a simple instruction-based rewriting approach achieves a significant anti-distillation effect. Notably, this method not only preserves the performance of the teacher model but can also enhance it. Additionally, we demonstrate that our rewriting approach allows for the embedding of watermarks that can be reliably detected, with virtually no false alarms. This capability ensures that even if a student model is created, it can be verified against the original teacher model.
Conclusion
The advancements in our research present a dual advantage: safeguarding the integrity of large language models against unauthorized knowledge distillation while simultaneously enhancing their performance. The methods we have developed show promise for wider application in the field of AI, particularly in protecting proprietary models from exploitation. For those interested in the technical details and implementation, our code is available at GitHub – Trace Rewriting.