  Detecting non-factual content is a longstanding goal to increase the
trustworthiness of large language models (LLMs) generations. Current factuality
probes, trained using humanannotated labels, exhibit limited transferability to
out-of-distribution content, while online selfconsistency checking imposes
extensive computation burden due to the necessity of generating multiple
outputs. This paper proposes PINOSE, which trains a probing model on offline
self-consistency checking results, thereby circumventing the need for
human-annotated data and achieving transferability across diverse data
distributions. As the consistency check process is offline, PINOSE reduces the
computational burden of generating multiple responses by online consistency
verification. Additionally, it examines various aspects of internal states
prior to response decoding, contributing to more effective detection of factual
inaccuracies. Experiment results on both factuality detection and question
answering benchmarks show that PINOSE achieves surpassing results than existing
factuality detection methods. Our code and datasets are publicly available on
this anonymized repository.
