  Pretrained language models often generate outputs that are not in line with
human preferences, such as harmful text or factually incorrect summaries.
Recent work approaches the above issues by learning from a simple form of human
feedback: comparisons between pairs of model-generated outputs. However,
comparison feedback only conveys limited information about human preferences.
In this paper, we introduce Imitation learning from Language Feedback (ILF), a
new approach that utilizes more informative language feedback. ILF consists of
three steps that are applied iteratively: first, conditioning the language
model on the input, an initial LM output, and feedback to generate refinements.
Second, selecting the refinement incorporating the most feedback. Third,
finetuning the language model to maximize the likelihood of the chosen
refinement given the input. We show theoretically that ILF can be viewed as
Bayesian Inference, similar to Reinforcement Learning from human feedback. We
evaluate ILF's effectiveness on a carefully-controlled toy task and a realistic
summarization task. Our experiments demonstrate that large language models
accurately incorporate feedback and that finetuning with ILF scales well with
the dataset size, even outperforming finetuning on human summaries. Learning
from both language and comparison feedback outperforms learning from each
alone, achieving human-level summarization performance.
