Code-on-Graph lets LLMs turn retrieved KG facts into Python class instances and generate executable code for reasoning, outperforming prior LLM-KG methods by up to 10.5% on WebQSP, CWQ, and GrailQA.
InProceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
2 Pith papers cite this work, alongside 83 external citations. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
CacheRAG is a cache-augmented architecture for LLM KGQA using ISR parsing, hierarchical MMR-based retrieval, and bounded subgraph expansion, claiming +13.2% accuracy gains on CRAG.
citing papers explorer
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Code-on-Graph: Iterative Programmatic Reasoning via Large Language Models on Knowledge Graphs
Code-on-Graph lets LLMs turn retrieved KG facts into Python class instances and generate executable code for reasoning, outperforming prior LLM-KG methods by up to 10.5% on WebQSP, CWQ, and GrailQA.
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CacheRAG: A Semantic Caching System for Retrieval-Augmented Generation in Knowledge Graph Question Answering
CacheRAG is a cache-augmented architecture for LLM KGQA using ISR parsing, hierarchical MMR-based retrieval, and bounded subgraph expansion, claiming +13.2% accuracy gains on CRAG.