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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