NeuronTune: Fine-Grained Neuron Modulation for Balanced Safety-Utility Alignment in LLMs
This addresses the critical issue of reliable LLM deployment by improving safety-utility alignment, though it appears incremental as it builds on existing neuron modulation techniques.
The paper tackles the problem of balancing safety and utility in Large Language Models (LLMs) by proposing NeuronTune, a fine-grained framework that dynamically modulates sparse neurons, resulting in significant outperformance over state-of-the-art methods with superior safety and maintained utility.
Ensuring robust safety alignment while preserving utility is critical for the reliable deployment of Large Language Models (LLMs). However, current techniques fundamentally suffer from intertwined deficiencies: insufficient robustness against malicious attacks, frequent refusal of benign queries, degradation in generated text quality and general task performance--the former two reflecting deficits in robust safety and the latter constituting utility impairment. We trace these limitations to the coarse-grained layer-wise interventions in existing methods. To resolve this, we propose NeuronTune, a fine-grained framework that dynamically modulates sparse neurons to achieve simultaneous safety-utility optimization. Our approach first identifies safety-critical and utility-preserving neurons across all layers via attribution, then employs meta-learning to adaptively amplify safety-neuron activations and suppress utility-neuron activations. Crucially, NeuronTune enables tunable adjustment of intervention scope via neuron-count thresholds, supporting flexible adaptation to security-critical or utility-priority scenarios. Extensive experimental results demonstrate that our method significantly outperforms existing state-of-the-art technologies, achieving superior model safety while maintaining excellent utility.