Refined Direct Preference Optimization with Synthetic Data for
  Behavioral Alignment of LLMs
Víctor Gallego
In this paper, we introduce \emph{refined Direct Preference Optimization}
(rDPO), a method for improving the behavioral alignment of Large Language
Models (LLMs) without the need for human-annotated data. The method involves
creating synthetic data using self-critique prompting by a teacher LLM and then
utilising a generalized DPO loss function to distil to a student LLM. The loss
function incorporates an additional external reward model to improve the
quality of synthetic data, making rDPO robust to potential noise in the
synthetic dataset. rDPO is shown to be effective in a diverse set of
behavioural alignment tasks, such as improved safety, robustness against
role-playing, and reduced sycophancy. Code to be released at
https://github.com/vicgalle/refined-dpo.