LogicSkills: A Structured Benchmark for Formal Reasoning in Large Language Models
This addresses the need for better evaluation of formal reasoning in AI, though it is incremental as it builds on existing benchmarks.
The authors tackled the problem of unclear logical reasoning skills in large language models by introducing LogicSkills, a structured benchmark isolating three fundamental formal reasoning skills, and found that models perform well on validity assessment but poorly on symbolization and countermodel construction.
Large language models have demonstrated notable performance across various logical reasoning benchmarks. However, it remains unclear which core logical skills they truly master. To address this, we introduce LogicSkills, a unified benchmark designed to isolate three fundamental skills in formal reasoning: (i) $\textit{formal symbolization}\unicode{x2014}$translating premises into first-order logic; (ii) $\textit{countermodel construction}\unicode{x2014}$formulating a finite structure in which all premises are true while the conclusion is false; and (iii) $\textit{validity assessment}\unicode{x2014}$deciding whether a conclusion follows from a given set of premises. Items are drawn from the two-variable fragment of first-order logic (without identity) and are presented in both natural English and a Carroll-style language with nonce words. All examples are verified for correctness and non-triviality using the SMT solver Z3. Across leading models, performance is high on validity but substantially lower on symbolization and countermodel construction, suggesting reliance on surface-level patterns rather than genuine symbolic or rule-based reasoning.