HOLMES is the first real-world benchmark for higher-order symbolic reasoning in LLMs, where models average 50.64% accuracy and the best reaches 59.54%.
Diagnosing the first-order logical reasoning ability through logicnli, in: Proceed- ings of the Conference on Empirical Methods in Natural Language Processing, pp
5 Pith papers cite this work. Polarity classification is still indexing.
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Audit finds 36-39% incorrect FOL labels in FOLIO and MALLS; corrections raise LLM accuracy 9-22 points and an LLM-guided review framework achieves 90% dataset quality after checking fewer than 24% of examples.
LGMT applies metamorphic testing derived from first-order logic equivalences to detect reasoning inconsistencies in LLMs that static benchmarks miss.
QMFOL generates monadic first-order logic tasks with controllable complexity via pattern-based structures and round-trip prover verification, then evaluates six LRMs showing performance drops as logical depth and width increase.
ChLogic benchmark shows persistent English-Chinese gaps in LLM logical reasoning performance, with back-translation effects varying by model and difficulty.
citing papers explorer
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HOLMES: Evaluating Higher-Order Logical Reasoning in LLMs
HOLMES is the first real-world benchmark for higher-order symbolic reasoning in LLMs, where models average 50.64% accuracy and the best reaches 59.54%.
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Fixing FOLIO and MALLS: Verified Annotations and an LLM-assisted Framework to Focus Human Relabeling
Audit finds 36-39% incorrect FOL labels in FOLIO and MALLS; corrections raise LLM accuracy 9-22 points and an LLM-guided review framework achieves 90% dataset quality after checking fewer than 24% of examples.
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LGMT: Logic-Grounded Metamorphic Testing for Evaluating the Reasoning Reliability of LLMs
LGMT applies metamorphic testing derived from first-order logic equivalences to detect reasoning inconsistencies in LLMs that static benchmarks miss.
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QMFOL: Benchmarking Large Language Model Reasoning via Quantifiable Monadic First-Order Logic Test Case Generation
QMFOL generates monadic first-order logic tasks with controllable complexity via pattern-based structures and round-trip prover verification, then evaluates six LRMs showing performance drops as logical depth and width increase.
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ChLogic: Evaluating Robustness of Logical Reasoning in Chinese Expressions
ChLogic benchmark shows persistent English-Chinese gaps in LLM logical reasoning performance, with back-translation effects varying by model and difficulty.