  Assessing factuality of text generated by large language models (LLMs) is an
emerging yet crucial research area, aimed at alerting users to potential errors
and guiding the development of more reliable LLMs. Nonetheless, the evaluators
assessing factuality necessitate suitable evaluation themselves to gauge
progress and foster advancements. This direction remains under-explored,
resulting in substantial impediments to the progress of factuality evaluators.
To mitigate this issue, we introduce a benchmark for Factuality Evaluation of
large Language Models, referred to as felm. In this benchmark, we collect
responses generated from LLMs and annotate factuality labels in a fine-grained
manner. Contrary to previous studies that primarily concentrate on the
factuality of world knowledge (e.g.~information from Wikipedia), felm focuses
on factuality across diverse domains, spanning from world knowledge to math and
reasoning. Our annotation is based on text segments, which can help pinpoint
specific factual errors. The factuality annotations are further supplemented by
predefined error types and reference links that either support or contradict
the statement. In our experiments, we investigate the performance of several
LLM-based factuality evaluators on felm, including both vanilla LLMs and those
augmented with retrieval mechanisms and chain-of-thought processes. Our
findings reveal that while retrieval aids factuality evaluation, current LLMs
are far from satisfactory to faithfully detect factual errors.
