  We fine-tune large language models to write natural language critiques
(natural language critical comments) using behavioral cloning. On a topic-based
summarization task, critiques written by our models help humans find flaws in
summaries that they would have otherwise missed. Our models help find naturally
occurring flaws in both model and human written summaries, and intentional
flaws in summaries written by humans to be deliberately misleading. We study
scaling properties of critiquing with both topic-based summarization and
synthetic tasks. Larger models write more helpful critiques, and on most tasks,
are better at self-critiquing, despite having harder-to-critique outputs.
Larger models can also integrate their own self-critiques as feedback, refining
their own summaries into better ones. Finally, we motivate and introduce a
framework for comparing critiquing ability to generation and discrimination
ability. Our measurements suggest that even large models may still have
relevant knowledge they cannot or do not articulate as critiques. These results
are a proof of concept for using AI-assisted human feedback to scale the
supervision of machine learning systems to tasks that are difficult for humans
to evaluate directly. We release our training datasets, as well as samples from
our critique assistance experiments.
