Towards a Unified View of Preference Learning for Large Language Models:
  A Survey
Authors: name: Bofei Gao, name: Feifan Song, name: Yibo Miao, name: Zefan Cai, name: Zhe Yang, name: Liang Chen, name: Helan Hu, name: Runxin Xu, name: Qingxiu Dong, name: Ce Zheng, name: Wen Xiao, name: Ge Zhang, name: Daoguang Zan, name: Keming Lu, name: Bowen Yu, name: Dayiheng Liu, name: Zeyu Cui, name: Jian Yang, name: Lei Sha, name: Houfeng Wang, name: Zhifang Sui, name: Peiyi Wang, name: Tianyu Liu, name: Baobao Chang

Large Language Models (LLMs) exhibit remarkably powerful capabilities. One of
the crucial factors to achieve success is aligning the LLM's output with human
preferences. This alignment process often requires only a small amount of data
to efficiently enhance the LLM's performance. While effective, research in this
area spans multiple domains, and the methods involved are relatively complex to
understand. The relationships between different methods have been
under-explored, limiting the development of the preference alignment. In light
of this, we break down the existing popular alignment strategies into different
components and provide a unified framework to study the current alignment
strategies, thereby establishing connections among them. In this survey, we
decompose all the strategies in preference learning into four components:
model, data, feedback, and algorithm. This unified view offers an in-depth
understanding of existing alignment algorithms and also opens up possibilities
to synergize the strengths of different strategies. Furthermore, we present
detailed working examples of prevalent existing algorithms to facilitate a
comprehensive understanding for the readers. Finally, based on our unified
perspective, we explore the challenges and future research directions for
aligning large language models with human preferences.