Diverse Preference Optimization
This addresses the need for varied outputs in creative generative tasks, offering an incremental improvement over existing preference optimization methods.
The paper tackles the problem of reduced diversity in language model outputs after post-training, introducing Diverse Preference Optimization (DivPO) to generate more diverse responses while maintaining quality, resulting in up to 74.6% increases in diversity metrics with similar win rates.
Post-training of language models, either through reinforcement learning, preference optimization or supervised finetuning, tends to sharpen the output probability distribution and reduce the diversity of generated responses. This is particularly a problem for creative generative tasks where varied responses are desired. In this work we introduce Diverse Preference Optimization (DivPO), an optimization method which learns to generate much more diverse responses than standard pipelines, while maintaining the quality of the generations. In DivPO, preference pairs are selected by first considering a pool of responses, and a measure of diversity among them, and selecting chosen examples as being more rare but high quality, while rejected examples are more common, but low quality. DivPO results in generating 45.6% more diverse persona attributes, and a 74.6% increase in story diversity, while maintaining similar win rates as standard baselines. On general instruction following, DivPO results in a 46.2% increase in diversity, and a 2.4% winrate improvement compared to DPO.