  Common methods for aligning large language models (LLMs) with desired
behaviour heavily rely on human-labelled data. However, as models grow
increasingly sophisticated, they will surpass human expertise, and the role of
human evaluation will evolve into non-experts overseeing experts. In
anticipation of this, we ask: can weaker models assess the correctness of
stronger models? We investigate this question in an analogous setting, where
stronger models (experts) possess the necessary information to answer questions
and weaker models (non-experts) lack this information. The method we evaluate
is debate, where two LLM experts each argue for a different answer, and a
non-expert selects the answer. We find that debate consistently helps both
non-expert models and humans answer questions, achieving 76% and 88% accuracy
respectively (naive baselines obtain 48% and 60%). Furthermore, optimising
expert debaters for persuasiveness in an unsupervised manner improves
non-expert ability to identify the truth in debates. Our results provide
encouraging empirical evidence for the viability of aligning models with debate
in the absence of ground truth.
