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cs.CL2024★ 1 cited
Improving Reward Models with Synthetic Critiques
Zihuiwen Ye, Fraser Greenlee-Scott, Max Bartolo +3
Reward models (RMs) play a critical role in aligning language models through the process of reinforcement learning from human feedback. RMs are trained to predict a score reflectin…
cs.CL2024
LLMCRIT: Teaching Large Language Models to Use Criteria
Weizhe Yuan, Pengfei Liu, Matthias Gallé
Humans follow criteria when they execute tasks, and these criteria are directly used to assess the quality of task completion. Therefore, having models learn to use criteria to pro…