An important issue with Large Language Models (LLMs) is their undesired ability to generate toxic language. In this work, we show that the neurons responsible for toxicity can be determined by their power to discriminate toxic sentences, and that toxic language can be mitigated by reducing their activation levels proportionally to this power. We propose AUROC adaptation (AurA), an intervention that can be applied to any pre-trained LLM to mitigate toxicity. As the intervention is proportional to the ability of each neuron to discriminate toxic content, it is free of any model-dependent hyperparameters. We show that AurA can achieve up to $2.2 \times$ reduction in toxicity with only a $0.72$ perplexity increase. We also show that AurA is effective with models of different scale (from 1.5B to 40B parameters), and its effectiveness in mitigating toxic language, while preserving common-sense zero-shot abilities, holds across all scales. AurA can be combined with pre-prompting strategies, boosting its average mitigation potential from $1.28\times$ to $2.35\times$. Moreover, AurA can counteract adversarial pre-prompts that maliciously elicit toxic content, making it an effective method for deploying safer and less toxic models.

通过降低与网络毒性内容识别相关的神经元的激活水平，我们提出了AUROC适应（AurA）的方法，可以减轻大型语言模型中的毒性，并且在保留常识零样本能力的同时，已经证明了其对不同规模的模型有效，并且即使结合了预提示策略，从而提高了平均缓解潜力。此外，AurA可以对抗恶意引导的毒性内容，并使模型更安全和更少有毒。

低调专家：语言模型中的毒性缓解的神经干预