Logic-of-Thought

💭 Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in LLMs

This research paper introduces Logic-of-Thought (LoT), a novel prompting method designed to enhance logical reasoning in large language models. LoT extracts propositions and logical relations from input text, expands them using logical rules, and reintegrates this information into the original prompt. Unlike existing techniques, LoT preserves information and guides the model's reasoning process while leveraging its natural language understanding. Experiments across multiple datasets demonstrate LoT's effectiveness in improving various prompting methods. The authors also compare LoT favorably to a neuro-symbolic approach, highlighting its advantages in information preservation and language comprehension utilization.

📎 Link to paper