Imagine a world where AI understands not just what we say, but also how much we agree on it. That's the exciting promise of new research on fine-tuning large language models (LLMs). Traditional methods assume everyone has the same level of agreement on any given topic. But what if some questions have clear-cut answers, while others are more subjective? This research introduces "MallowsPO," a novel approach that factors in the *dispersion* of human preferences. Think of it like this: people generally agree on "1+1=2," but have varying opinions on the "best city to live in." MallowsPO leverages this difference by assigning a "dispersion index" to each prompt. This index reflects how much disagreement exists around a particular question. By incorporating this index, MallowsPO fine-tunes LLMs more effectively, leading to better performance across various tasks. From synthetic tests to real-world applications like controllable text generation and dialogue, MallowsPO consistently outperforms traditional methods. This breakthrough opens doors to more nuanced and human-like AI interactions. It allows LLMs to navigate the complexities of subjective topics, generating responses that are both informative and sensitive to diverse perspectives. While challenges remain, MallowsPO represents a significant step towards building AI that truly understands the spectrum of human thought.