KARPA: A Training-free Method of Adapting Knowledge Graph as References for Large Language Model's Reasoning Path Aggregation
Keywords: Knowledge Graph, Large Language Models, Chain-of-Thought, Reasoning
TL;DR: We introduce KG Assisted Reasoning Path Aggregation (KARPA), a novel framework that enhances LLM-based KG reasoning by leveraging the global planning capabilities of LLMs, enabling efficient and accurate question answering without training process.
Abstract: Large language models (LLMs) demonstrate exceptional performance across a variety of tasks, yet they are often affected by hallucinations and the timeliness of knowledge. Leveraging knowledge graphs (KGs) as external knowledge sources has emerged as a viable solution, but existing methods for LLM-based knowledge graph question answering (KGQA) are often limited by step-by-step decision-making on KGs, restricting the global planning and reasoning capabilities of LLMs, or they require fine-tuning or pre-training on specific KGs. To address these challenges, we propose Knowledge graph Assisted Reasoning Path Aggregation (KARPA), a novel framework that harnesses the global planning abilities of LLMs for efficient and accurate KG reasoning on KGs. KARPA operates through a three-step process: pre-planning, retrieving, and reasoning. First, KARPA uses the LLM's global planning ability to pre-plan logically coherent relation paths based on the provided question and relevant relations within the KG. Next, in the retrieving phase, relation paths with high semantic similarity to the pre-planned paths are extracted as candidate paths using a semantic embedding model. Finally, these candidate paths are provided to the LLM for comprehensive reasoning. Unlike existing LLM-based KGQA methods, KARPA fully leverages the global planning and reasoning capabilities of LLMs without requiring stepwise traversal or additional training, and it is compatible with various LLM architectures. Extensive experimental results show that KARPA achieves state-of-the-art performance in KGQA tasks, delivering both high efficiency and accuracy.
Primary Area: neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)
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Submission Number: 14243
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