Large language models (LLMs) have an unfortunate tendency to generate toxic language, posing a major challenge for their safe deployment. New research explores how to mitigate this issue by targeting the very neurons within the model that contribute to toxic output. The technique, called AUROC adaptation (AURA), identifies and then dampens the activation of these "toxic neurons." Researchers found that by subtly reducing the influence of these neurons, they could significantly decrease the model's output of toxic language without impacting the model’s ability to perform other tasks, like common sense reasoning. This breakthrough offers a promising path towards creating safer, more responsible AI systems. It works by treating each neuron in the model as a potential classifier for toxic language. By analyzing how strongly each neuron reacts to toxic sentences, researchers can pinpoint the "experts" most responsible for generating such content. AURA then strategically scales down the activation of these expert neurons, effectively "whispering" to them to reduce their toxic influence. Testing across a range of LLMs, AURA consistently reduced toxic language by a significant margin, even when given prompts specifically designed to elicit offensive responses. This approach offers several advantages. First, it can be applied to any pre-trained LLM without the need for computationally expensive retraining. Second, it's hyperparameter-free, meaning it doesn't require tedious tweaking to work effectively. While the research focused on toxicity, AURA could potentially be adapted to mitigate other unwanted behaviors in LLMs, like generating biased or misleading information. However, this raises the question of what constitutes “toxic” language and who gets to decide. While AURA is a step forward in ensuring responsible AI use, continued research and open discussion are crucial to address its potential implications.