Bo Dong

Cross-Relational Preference Learning for Better LLM Instruction Following

This work proposes Cross-Relational Preference Learning (CRPL), a novel framework for constructing preference data that explicitly models inter-instruction relationships through two key techniques: Cross-Relationship Perturbation and Cross-Region Pair Sampling, which enables the generation of more diverse preference data that captures a wide spectrum of constraint variations.