Light Alignment Improves LLM Safety via Model Self-Reflection with a Single Neuron
This addresses the need for efficient and practical safety alignment for LLM deployment, though it appears incremental as it builds on existing lightweight approaches.
The paper tackles the problem of computationally expensive and poorly generalizing safety alignment for large language models (LLMs) by proposing a lightweight decoding method that uses a single neuron as a gating mechanism, achieving enhanced safety while preserving utility with low training overhead and improved generalization across model scales.
The safety of large language models (LLMs) has increasingly emerged as a fundamental aspect of their development. Existing safety alignment for LLMs is predominantly achieved through post-training methods, which are computationally expensive and often fail to generalize well across different models. A small number of lightweight alignment approaches either rely heavily on prior-computed safety injections or depend excessively on the model's own capabilities, resulting in limited generalization and degraded efficiency and usability during generation. In this work, we propose a safety-aware decoding method that requires only low-cost training of an expert model and employs a single neuron as a gating mechanism. By effectively balancing the model's intrinsic capabilities with external guidance, our approach simultaneously preserves utility and enhances output safety. It demonstrates clear advantages in training overhead and generalization across model scales, offering a new perspective on lightweight alignment for the safe and practical deployment of large language models. Code: https://github.com/Beijing-AISI/NGSD.