September 20, 2025

Enhancing the Logical Reasoning Abilities of Large Language Models

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This research identifies challenges in logical reasoning for LLMs, suggesting enhanced training using modal logic and curriculum learning.

Large language models improve with targeted training methods, focusing on logical reasoning capabilities.
The proposed training method distinguishes causal relationships from correlations, enhancing performance in sentiment classification.
Challenges include identifying logical inconsistencies across questions, which can be addressed with phased training.
Incorporating modal and epistemic logic aids in tackling complex reasoning tasks, broadening LLM capabilities.

Fengxiang Cheng (2025) studied this question.

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This research identifies challenges in logical reasoning for LLMs, suggesting enhanced training using modal logic and curriculum learning.