  Many recent advances in natural language generation have been fueled by
training large language models on internet-scale data. However, this paradigm
can lead to models that generate toxic, inaccurate, and unhelpful content, and
automatic evaluation metrics often fail to identify these behaviors. As models
become more capable, human feedback is an invaluable signal for evaluating and
improving models. This survey aims to provide an overview of the recent
research that has leveraged human feedback to improve natural language
generation. First, we introduce an encompassing formalization of feedback, and
identify and organize existing research into a taxonomy following this
formalization. Next, we discuss how feedback can be described by its format and
objective, and cover the two approaches proposed to use feedback (either for
training or decoding): directly using the feedback or training feedback models.
We also discuss existing datasets for human-feedback data collection, and
concerns surrounding feedback collection. Finally, we provide an overview of
the nascent field of AI feedback, which exploits large language models to make
judgments based on a set of principles and minimize the need for human
intervention.
