Aligning large language models (LLMs) with human expectations without
human-annotated preference data is an important problem. In this paper, we
propose a method to evaluate the response preference by using the output
probabilities of response pairs under contrastive prompt pairs, which could
achieve better performance on LLaMA2-7B and LLaMA2-13B compared to RLAIF. Based
on this, we propose an automatic alignment method, Direct Large Model Alignment
(DLMA). First, we use contrastive prompt pairs to automatically generate
preference data. Then, we continue to evaluate the generated preference data
using contrastive prompt pairs and calculate a self-rewarding score. Finally,
we use the DPO algorithm to effectively align LLMs by combining this
self-rewarding score. In the experimental stage, our DLMA method could surpass
the \texttt{RLHF} method without relying on human-annotated preference data.

通过对比提示对生成的偏好数据进行评估，并计算自奖励分数，最终使用 DPO 算法结合此自奖励分数来有效地对齐大型语言模型，实现了不依赖人工标注的偏好数据的 DLMA 方法能够超越 RLHF 方法。