  As AI systems are used to answer more difficult questions and potentially
help create new knowledge, judging the truthfulness of their outputs becomes
more difficult and more important. How can we supervise unreliable experts,
which have access to the truth but may not accurately report it, to give
answers that are systematically true and don't just superficially seem true,
when the supervisor can't tell the difference between the two on their own? In
this work, we show that debate between two unreliable experts can help a
non-expert judge more reliably identify the truth. We collect a dataset of
human-written debates on hard reading comprehension questions where the judge
has not read the source passage, only ever seeing expert arguments and short
quotes selectively revealed by 'expert' debaters who have access to the
passage. In our debates, one expert argues for the correct answer, and the
other for an incorrect answer. Comparing debate to a baseline we call
consultancy, where a single expert argues for only one answer which is correct
half of the time, we find that debate performs significantly better, with 84%
judge accuracy compared to consultancy's 74%. Debates are also more efficient,
being 68% of the length of consultancies. By comparing human to AI debaters, we
find evidence that with more skilled (in this case, human) debaters, the
performance of debate goes up but the performance of consultancy goes down. Our
error analysis also supports this trend, with 46% of errors in human debate
attributable to mistakes by the honest debater (which should go away with
increased skill); whereas 52% of errors in human consultancy are due to
debaters obfuscating the relevant evidence from the judge (which should become
worse with increased skill). Overall, these results show that debate is a
promising approach for supervising increasingly capable but potentially
unreliable AI systems.
