FOLIO: Natural Language Reasoning with First-Order Logic

Key Points

To introduce FOLIO, a benchmark dataset designed to evaluate and support natural language reasoning grounded in first-order logic formalisms.
Constructed complex natural language reasoning problems paired with expert-annotated first-order logic representations for premises and conclusions.
Evaluated modern natural language processing architectures on deductive reasoning and logical equivalence tasks.
Language models exhibit marked performance degradation when tasked with multi-step logical deduction compared to standard textual inference benchmarks.
Coupling natural language prompts with formal first-order logic representations improves reasoning consistency and deductive accuracy.

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Benchmark study reveals reasoning performance deficits in language models, indicating difficulties with multi-step first-order logic deduction.

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Han et al. (2024) studied this question.

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