Knowledge Graph Prompting Sparks Graph of Thoughts in LLMs

This paper connects knowledge graph (KG) prompting with graph-of-thought (GoT) reasoning in LLMs. Rather than prompting models with flat text, the approach encodes relational structure from knowledge graphs as explicit reasoning scaffolds, allowing LLMs to follow graph-structured inference chains rather than linear chains of thought.

The work bridges structured knowledge representation and the emerging line of graph-structured prompting methods (extending beyond CoT and ToT).




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