  Large language models (LLMs) have achieved remarkable performance on a
variety of natural language understanding tasks. However, existing benchmarks
are inadequate in measuring the complex logical reasoning capabilities of a
model. We present FOLIO, a human-annotated, logically complex and diverse
dataset for reasoning in natural language (NL), equipped with first-order logic
(FOL) annotations. FOLIO consists of 1,430 examples (unique conclusions), each
paired with one of 487 sets of premises used to deductively reason for the
validity of each conclusion. The logical correctness of the premises and
conclusions is ensured by their FOL annotations, which are automatically
verified by an FOL inference engine. In addition to the main NL reasoning task,
NL-FOL pairs in FOLIO constitute a new NL-FOL translation dataset. Our
experiments on FOLIO systematically evaluate the FOL reasoning ability of
supervised fine-tuning on medium-sized language models. For both NL reasoning
and NL-FOL translation, we benchmark multiple state-of-the-art language models.
Our results show that a subset of FOLIO presents a challenge for one of the
most capable {Large Language Model (LLM)} publicly available, GPT-4.
