Hiroshi Takahashi

This paper proposes a distribution shift adaptation method for UU learning that uses UU data in the training distribution and a few UU data in the test distribution based on the importance weighting, which minimizes the test risk by using training data with estimated importance weights.

Aligning language models with human preferences just got more stable: a new method avoids the divergence problems of direct density ratio optimization.