  Alignment is the most critical step in building large language models (LLMs)
that meet human needs. With the rapid development of LLMs gradually surpassing
human capabilities, traditional alignment methods based on human-annotation are
increasingly unable to meet the scalability demands. Therefore, there is an
urgent need to explore new sources of automated alignment signals and technical
approaches. In this paper, we systematically review the recently emerging
methods of automated alignment, attempting to explore how to achieve effective,
scalable, automated alignment once the capabilities of LLMs exceed those of
humans. Specifically, we categorize existing automated alignment methods into 4
major categories based on the sources of alignment signals and discuss the
current status and potential development of each category. Additionally, we
explore the underlying mechanisms that enable automated alignment and discuss
the essential factors that make automated alignment technologies feasible and
effective from the fundamental role of alignment.
