Language Model Detoxification

Language model detoxification aims to mitigate the generation of offensive or harmful content by large language models, improving their safety and reliability for real-world applications. Current research focuses on methods like fine-tuning, decoding modifications, and reinforcement learning, often exploring techniques to manipulate the model's internal representations or incorporate external resources such as toxic corpora to improve control over generated text. These efforts are crucial for responsible deployment of powerful language models, addressing ethical concerns and promoting the development of more beneficial AI systems.