  Natural language (NL) feedback offers rich insights into user experience.
While existing studies focus on an instance-level approach, where feedback is
used to refine specific examples, we introduce a framework for system-level use
of NL feedback. We show how to use feedback to formalize system-level design
decisions in a human-in-the-loop-process -- in order to produce better models.
In particular this is done through: (i) metric design for tasks; and (ii)
language model prompt design for refining model responses. We conduct two case
studies of this approach for improving search query and dialog response
generation, demonstrating the effectiveness of system-level feedback. We show
the combination of system-level and instance-level feedback brings further
gains, and that human written instance-level feedback results in more grounded
refinements than GPT-3.5 written ones, underlying the importance of human
feedback for building systems. We release our code and data at
https://github.com/yyy-Apple/Sys-NL-Feedback.
