Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models

Understand

Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks but their performance in complex logical reasoning tasks remains unsatisfactory.

• Although some prompting methods, such as Chain-of-Thought, can improve the reasoning ability of LLMs to some extent, they suffer from an unfaithful issue where derived conclusions may not align with the generated reasoning chain.
• To address this issue, some studies employ the approach of propositional logic to further enhance logical reasoning abilities of LLMs.
• However, the potential omissions in the extraction of logical expressions in these methods can cause information loss in the logical reasoning process, thereby generating incorrect results.