Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation

Understand

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.