To combat the overly polite and predictable language of AI models, a method using Shannon Entropy is proposed to filter out low-entropy responses, which are seen as aesthetically unappealing. This approach measures the "messiness" of text, with professional technical prose being high in entropy, whereas AI-generated text often has low entropy due to its predictability. By implementing a system that blocks responses with an entropy below 3.5, the method aims to create a dataset of rejected and chosen responses to train AI models to produce more natural and less sycophantic language. This technique is open-source and available in Steer v0.4, and it provides a novel way to refine AI communication by focusing on the mathematical properties of text. This matters because it offers a new approach to improving AI language models by enhancing their ability to produce more human-like and less formulaic responses.
