Large language models (LLMs) are impressive feats of AI engineering, capable of generating human-like text. However, they sometimes exhibit a dark side: generating toxic or biased content. This toxicity stems from the data they're trained on, which often includes harmful language from the internet. New research introduces a clever approach called EXPOSED (EXPert-guided extinction Of toxic tokens for debiaSED generation) to combat this problem. Instead of trying to create a perfectly clean dataset, which is incredibly difficult, EXPOSED leverages the readily available *toxic* data to train a 'debiasing expert.' This expert acts like a toxicity detector, identifying potentially harmful words or phrases during the text generation process. Then, EXPOSED cleverly adjusts the LLM's output, suppressing the toxic tokens and boosting safer alternatives. This method has been tested across several popular LLM families, including GPT-Neo, FLAN-T5, and LLaMA-2, showing promising results in reducing toxicity while maintaining fluent and coherent text generation. The research also explores the delicate balance between removing toxicity and preserving the LLM's overall performance. While EXPOSED represents a significant step forward, challenges remain. The effectiveness of the debiasing expert depends on the quality of the toxic data it's trained on. Further research is needed to refine these techniques and ensure that AI language models become truly beneficial tools, free from harmful biases.