Lucy Sun


2024

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FOLIO: Natural Language Reasoning with First-Order Logic
Simeng Han | Hailey Schoelkopf | Yilun Zhao | Zhenting Qi | Martin Riddell | Wenfei Zhou | James Coady | David Peng | Yujie Qiao | Luke Benson | Lucy Sun | Alexander Wardle-Solano | Hannah Szabó | Ekaterina Zubova | Matthew Burtell | Jonathan Fan | Yixin Liu | Brian Wong | Malcolm Sailor | Ansong Ni | Linyong Nan | Jungo Kasai | Tao Yu | Rui Zhang | Alexander Fabbri | Wojciech Kryscinski | Semih Yavuz | Ye Liu | Xi Lin | Shafiq Joty | Yingbo Zhou | Caiming Xiong | Rex Ying | Arman Cohan | Dragomir Radev
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

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 remains a challenge for one of the most capable Large Language Model (LLM) publicly available, GPT-4.