Logic-of-Thought: Enhancing Logical Reasoning in Large Language Models through Propositional Logic Augmentation

Practical Solutions to Enhance Logical Reasoning in Large Language Models

Overview:

Large Language Models (LLMs) excel in NLP tasks but struggle with math and logic. The Logic-of-Thought (LoT) method overcomes this by integrating symbolic reasoning with LLMs.

Solutions Highlight:

• CoT prompting breaks down problems into steps for logical reasoning enhancement.
• Neuro-symbolic methods like LReasoner and SatLM integrate symbolic reasoning with LLMs.
• LoT addresses information loss by adding logical insight to LLM reasoning, boosting performance.

Key Phases of LoT Framework:

• Logic Extraction: Identify logical elements in input context.
• Logic Extension: Expand logical expressions using reasoning laws.
• Logic Translation: Convert expanded logic back to natural language for LLMs.

Impact and Integration:

LoT significantly improves logical reasoning across datasets and integrates seamlessly with other methods, showing robust performance.

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