An optimization approach that bridges the gap between training and inference techniques via a highly detailed taxonomy of data characteristics to explicitly control generation attributes and implicitly condition generations during inference.

We propose an inference-time intervention framework based on Optimal Transport that generalizes previous methods and allows interpretable control of both LLMs and Diffusion models.