  A prominent weakness of modern language models (LMs) is their tendency to
generate factually incorrect text, which hinders their usability. A natural
question is whether such factual errors can be detected automatically. Inspired
by truth-seeking mechanisms in law, we propose a factuality evaluation
framework for LMs that is based on cross-examination. Our key idea is that an
incorrect claim is likely to result in inconsistency with other claims that the
model generates. To discover such inconsistencies, we facilitate a multi-turn
interaction between the LM that generated the claim and another LM (acting as
an examiner) which introduces questions to discover inconsistencies. We
empirically evaluate our method on factual claims made by multiple recent LMs
on four benchmarks, finding that it outperforms existing methods and baselines,
often by a large gap. Our results demonstrate the potential of using
interacting LMs for capturing factual errors.
