CLEAR: Continuous Latent Adapter Routing for Utility-Preserving LLM Safety Alignment
Researchers propose a new framework called CLEAR to improve the safety of large language models (LLMs) without sacrificing their utility. The approach uses a lightweight gate to control the activation strength of a safety adapter and reduce harmful model responses. Experiments show that CLEAR improves robustness on safety benchmarks while minimizing performance degradation on benign inputs.
Researchers propose a new framework called CLEAR to improve the safety of large language models (LLMs) without sacrificing their utility. The approach uses a lightweight gate to control the activation strength of a safety adapter and reduce harmful model responses. Experiments show that CLEAR improves robustness on safety benchmarks while minimizing performance degradation on benign inputs.
---
Why it matters: This matters because current methods for improving LLM safety often come at the cost of reducing their utility, which can be problematic in real-world applications. CLEAR offers a promising solution to this trade-off by providing a more targeted approach to safety adaptation.
Source: https://arxiv.org/abs/2608.21278
This article was originally published at: https://arxiv.org/abs/2608.21278