Huiyu Bai

GEM: Generative Entropy-Guided Preference Modeling for Few-Shot Alignment of LLMs

Alignment of large language models (LLMs) with human preferences typically relies on supervised reward models or external judges that demand abundant annotations. However, in fields that rely on professional knowledge, such as medicine and law, such large-scale preference labels are often unachievab