  Data annotation generally refers to the labeling or generating of raw data
with relevant information, which could be used for improving the efficacy of
machine learning models. The process, however, is labor-intensive and costly.
The emergence of advanced Large Language Models (LLMs), exemplified by GPT-4,
presents an unprecedented opportunity to automate the complicated process of
data annotation. While existing surveys have extensively covered LLM
architecture, training, and general applications, we uniquely focus on their
specific utility for data annotation. This survey contributes to three core
aspects: LLM-Based Annotation Generation, LLM-Generated Annotations Assessment,
and LLM-Generated Annotations Utilization. Furthermore, this survey includes an
in-depth taxonomy of data types that LLMs can annotate, a comprehensive review
of learning strategies for models utilizing LLM-generated annotations, and a
detailed discussion of the primary challenges and limitations associated with
using LLMs for data annotation. Serving as a key guide, this survey aims to
assist researchers and practitioners in exploring the potential of the latest
LLMs for data annotation, thereby fostering future advancements in this
critical field.
