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KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graph

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arxiv 2402.11163 v1 pith:M33O4JFV submitted 2024-02-17 cs.CL

Jinhao Jiang , Kun Zhou , Wayne Xin Zhao , Yang Song , Chen Zhu , Hengshu Zhu , Ji-Rong Wen This is my paper
classification cs.CL
keywords reasoningkg-agentknowledgellmsagentautonomouscomplexdata
verification ladder T0 review T1 audit T2 compute T3 formal
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In this paper, we aim to improve the reasoning ability of large language models (LLMs) over knowledge graphs (KGs) to answer complex questions. Inspired by existing methods that design the interaction strategy between LLMs and KG, we propose an autonomous LLM-based agent framework, called KG-Agent, which enables a small LLM to actively make decisions until finishing the reasoning process over KGs. In KG-Agent, we integrate the LLM, multifunctional toolbox, KG-based executor, and knowledge memory, and develop an iteration mechanism that autonomously selects the tool then updates the memory for reasoning over KG. To guarantee the effectiveness, we leverage program language to formulate the multi-hop reasoning process over the KG, and synthesize a code-based instruction dataset to fine-tune the base LLM. Extensive experiments demonstrate that only using 10K samples for tuning LLaMA-7B can outperform state-of-the-art methods using larger LLMs or more data, on both in-domain and out-domain datasets. Our code and data will be publicly released.

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Cited by 4 Pith papers

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  3. Search-on-Graph: Iterative Informed Navigation for Large Language Model Reasoning on Knowledge Graphs

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    An LLM that iteratively inspects 1-hop neighbors of a knowledge-graph entity and chooses the next relation achieves state-of-the-art KGQA scores on six Freebase/Wikidata benchmarks without fine-tuning.

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