LiPO: Listwise Preference Optimization through Learning-to-Rank

Aligning language models (LMs) with curated human feedback is critical to control their behaviors in real-world applications. Several recent policy optimization methods, such as DPO and SLiC, serve as promising alternatives to the traditional Reinforcement Learning from Human Feedback (RLHF) approach. In practice, human feedback often comes in a format of a ranked list over multiple responses to amortize the cost of reading prompt. Multiple responses can also be ranked by reward models or AI feedback. There lacks such a …

applications control cs.cl cs.lg feedback format human human feedback language language models learning-to-rank list lms optimization policy practice reinforcement reinforcement learning rlhf serve through world