New preprint using KG structure as inspiration for reasoning

Our new preprint simulates the thinking process of the knowledge graph constructors and utilizes the KG structure as inspiration for reasoning. It populates the sparse KG with hypothetical knowledge connections, concretizes the internal knowledge of LLMs, and constructs counterfactual reasoning chains.

🚀 Retrieve knowledge as a high-level hint for faithful reasoning, combine parametric and non-parametric knowledge 🚀 Prove the ability of LLM to conduct deductive reasoning using very limited information, provide a new perspective for inference time computation 🚀 Especially useful in scientific domains where the prompts could be knowledge and reasoning intensive, but building inclusive databases is expensive or infeasible.