data augmentation (and curation)
Several methods have
improved LLMs by (self-)creating training data to augment fine-tuning. Self-Instruct [Wang
et al., 2022] is a method for self-instruction creation of prompts and responses, which can
be used to improve a base LLM. We make use of a similar technique in our work, and then
use our self-reward model to score them. Several approaches have also created training data
by distilling from powerful LLMs, and shown a weaker LLM can then perform well. For
example, Al