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Small variations in prompt characteristics can meaningfully alter the semantics, tone, and contextual assumptions of large language model outputs. Such characteristics can involve group variables such as demographics or other sensitive latent attributes. Sensitivity to such input variables is undesirable when demograph...
This work proposes a general RL-based framework for Distribution Matching allowing matching the distribution of a latent categorical attribute of model outputs to a specified target distribution and proposes reward functions for other divergences such as KL and Jensen-Shannon and motivate them with theoretical justific...
It is shown that the recovered reward improves a base policy without a supervised loss and yields further gains when optimized after standard supervised fine-tuning and can be used for contextual alignment, in which a single policy can be tailored to the preferences of different audiences.
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