October 2, 2025Open Access

DivLogicEval: A Framework for Benchmarking Logical Reasoning Evaluation in Large Language Models

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DivLogicEval provides a more reliable evaluation of logical reasoning in language models.
The new benchmark incorporates diverse statements to improve language variety and accuracy.
Experiments reveal the logical reasoning required to answer questions within DivLogicEval.
A new evaluation metric reduces bias and randomness affecting language models' performances.

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A framework introduces a new evaluation metric for logical reasoning in language models, addressing benchmark limitations.

Chung et al. (2025) studied this question.

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