  The increased use of large language models (LLMs) across a variety of
real-world applications calls for mechanisms to verify the factual accuracy of
their outputs. In this work, we present a holistic end-to-end solution for
annotating the factuality of LLM-generated responses, which encompasses a
multi-stage annotation scheme designed to yield detailed labels concerning the
verifiability and factual inconsistencies found in LLM outputs. We further
construct an open-domain document-level factuality benchmark in three-level
granularity: claim, sentence and document, aiming to facilitate the evaluation
of automatic fact-checking systems. Preliminary experiments show that FacTool,
FactScore and Perplexity.ai are struggling to identify false claims, with the
best F1=0.63 by this annotation solution based on GPT-4. Annotation tool,
benchmark and code are available at https://github.com/yuxiaw/Factcheck-GPT.
