IC-590Tuning only the identified safety neurons (SN-Tune) reduces harmful scores of instruction-tuned and base models by over 90 points while preserving general capability.

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Yiran Zhao, Wenxuan Zhang, Yuxi Xie, Anirudh Goyal, Kenji Kawaguchi, Michael Shieh

SourceUnderstanding and Enhancing Safety Mechanisms of LLMs via Safety-Specific Neuron

The authors fine-tune only the identified safety neurons (setting gradients of all other parameters to zero) on 50 safety documents for 1 epoch at learning rate 1e-6. For instruction-tuned models, this reduces average harmful scores from 93.5 to 3.0 (Vicuna-13b-v1.5), 65.5 to 2.0 (Llama3-8b-instruction), and 70.8 to 4.5 (Mistral-7b-instruct-v0.2). For base models, it reduces scores from 98.8 to 5.3 (Llama2-7b-base), 100.0 to 13.5 (Llama3-8b-base), and 100.0 to 13.8 (Mistral-7b-v0.1). General capability (MMLU, ARC-C, GSM8K) is largely preserved or slightly improved. Training takes only 2-4 minutes versus 23-43 minutes for the Circ-Break baseline.

Evidence
interventional
Key metric
harmful score 93.5→3.0 (Vicuna-13b-v1.5), 65.5→2.0 (Llama3-8b-instruction), 70.8→4.5 (Mistral-7b-instruct-v0.2); 98.8→5.3 (Llama2-7b-base), 100.0→13.5 (Llama3-8b-base), 100.0→13.8 (Mistral-7b-v0.1); training cost 2-4 min vs 23-43 min for circ-break
Caveat
Evaluation uses attack success rate on Harmful Behavior testset with 4 attack methods; the safety corpus is only 50 documents from Zou et al. (2024).
Model
Vicuna Vicuna-13b-v1.5, Mistral 7B / Mistral / Mistral 3 7B / Mistral-0.2-7B / Mistral-v0.1 Mistral 7B Instruct v0.2, Mistral 7B v0.1, Llama 2 / Llama 2 base Llama2-7b-base, Llama 3 8B Base
Datasets
MMLU / MMLU-Math [eval], ARC-Challenge [eval], GSM8K [eval]
Related findings
IC-591
Extraction
automatic-extraction