Stepwise Reasoning Enhancement for LLMs via External Subgraph Generation
Quick Answer
The SGR framework enhances large language models by integrating external knowledge graphs through query-relevant subgraph generation, improving reasoning accuracy on benchmarks like CWQ and WebQSP.
Quick Take
Experiments show significant performance gains, with schema guidance and Neo4j retrieval being critical for effectiveness.
Key Points
- SGR improves reasoning accuracy and Hits@1 performance on CWQ, WebQSP, GrailQA, and KQA Pro.
- The framework constructs structured schemas from input questions to guide reasoning.
- Subgraphs provide explicit relational evidence for step-by-step reasoning.
- Collaborative reasoning integrates multiple paths for validating candidate answers.
- Ablation studies confirm the importance of schema guidance and Neo4j-based retrieval.
Paper Resources
Source Excerpt
From the original publisher, up to about 700 charactersarXiv:2606. 04454v1 Announce Type: new Abstract: have shown strong performance in natural language generation and downstream reasoning tasks, but they still struggle with logical consistency, factual grounding, and interpretability in complex multi-step reasoning. To address these limitations, this paper proposes SGR, a stepwise reasoning enhancement framework that integrates large language models with external knowledge graphs through query-relevant subgraph generation. …
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