Synthetic Preference

Synthetic preference learning aims to automate the creation of preference datasets for training and aligning large language models (LLMs), reducing the reliance on expensive and time-consuming human annotation. Current research focuses on generating synthetic preferences using multi-agent LLM workflows, leveraging techniques like preference optimization and reward modeling, often incorporating aspects like constitutional AI principles and multi-view learning to improve data quality and reduce noise. This approach holds significant promise for accelerating LLM development and improving their safety and alignment with human values, particularly in applications requiring fine-grained control over model behavior and safety configurations.