We trained “critique-writing” models to describe flaws in summaries. Human evaluators find flaws in summaries much more often when shown our model’s critiques. Larger models are better at self-critiquing, with scale improving critique-writing more than summary-writing. This shows promise for using AI systems to assist human supervision of AI systems on difficult tasks.
We want to ensure that future AI systems performing very difficult tasks remain aligned with human intent. Manypreviousworks on aligning language models rely on human evaluations as a training signal. However, humans struggle at evaluating very difficult tasks—for example, it is hard to spot every bug in a codebase or every factual error in a long essay. Models may then learn to give outputs that look good to humans but have errors we systematically fail to notice.
To mitigate this problem, we want to train AI assistants that help humans provide feedback on hard tasks. These assistants should point out flaws, help humans understand what’s going on, and answer their questions. An example of this is our past work on book summarization: reading the entire book is a lot of work, but humans assisted with chapter summaries have a much easier time evaluating a book summary.
As a proof of concept, we used supervised learning to train language models to write critiques of topic-based summaries of short stories, Wikipedia articles, and other texts from the internet. We use these models to assist human evaluators and study scaling properties of critique writing.