Direct Density Ratio Optimization: A Statistically Consistent Approach to Aligning Large Language Models

Aligning large language models (LLMs) with human preferences is crucial for safe deployment, yet existing methods assume specific preference models like Bradley-Terry model.This assumption leads to statistical inconsistency, where more data doesn't guarantee convergence to true human preferences.To address this critical gap, we introduce a novel alignment method Direct Density Ratio Optimization (DDRO).DDRO directly estimates the density ratio between preferred and unpreferred output distributions, circumventing the need for explicit human preference modeling.We theoretically prove that DDRO is statistically consistent, ensuring convergence to the true preferred distribution as the data size grows, regardless of the underlying preference structure.Experiments demonstrate that DDRO achieves superior performance compared to existing methods, showcasing its effectiveness and potential for significant improvement.DDRO unlocks the potential for truly data-driven alignment, paving the way for more reliable and human-aligned LLMs.

Large Language Models (LLMs) are useful, but they can sometimes generate content that is misaligned with human intent or preferences and may be harmful. Previous approaches to aligning LLMs with human preferences often fit complex human tastes into predefined and possibly oversimplified structures. This presents a fundamental challenge: even with extensive training data, LLMs might not truly learn desired responses. To address this issue, our research proposes a new LLM alignment method called Direct Density Ratio Optimization (DDRO). DDRO provides a way to guide LLMs towards human preferences without needing to rely on these predefined and potentially restrictive assumptions about the nature of human preferences. It learns directly from examples of preferred and unpreferred responses, allowing for a more flexible and data-driven alignment with genuine human tastes.A key aspect of DDRO is its theoretical guarantee: as training data increases, the LLM will generate responses more accurately reflecting true human preferences. This property of our method helps develop LLMs better aligned with human values, fostering safer, more reliable, and truly beneficial AI systems.