  With advancements in self-supervised learning, the availability of trillions
tokens in a pre-training corpus, instruction fine-tuning, and the development
of large Transformers with billions of parameters, large language models (LLMs)
are now capable of generating factual and coherent responses to human queries.
However, the mixed quality of training data can lead to the generation of
undesired responses, presenting a significant challenge. Over the past two
years, various methods have been proposed from different perspectives to
enhance LLMs, particularly in aligning them with human expectation. Despite
these efforts, there has not been a comprehensive survey paper that categorizes
and details these approaches. In this work, we aim to address this gap by
categorizing these papers into distinct topics and providing detailed
explanations of each alignment method, thereby helping readers gain a thorough
understanding of the current state of the field.
