KARPA: A Training-free Method of Adapting Knowledge Graph as References for Large Language Model's Reasoning Path Aggregation

TL;DR

KARPA is a training-free framework that leverages large language models' global planning to efficiently and accurately reason over knowledge graphs for question answering, avoiding step-by-step traversal and fine-tuning.

Contribution

It introduces a novel, training-free method that uses LLMs' global planning for knowledge graph reasoning, enhancing efficiency and adaptability without additional training.

Findings

Achieves state-of-the-art performance on KGQA tasks.

Operates without fine-tuning or pre-training on specific KGs.

Provides high efficiency and accuracy in reasoning over knowledge graphs.

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. KARPA operates in three steps: pre-planning relation paths using the LLM's global planning capabilities, matching semantically relevant paths via an embedding model, and reasoning over these paths to generate answers. Unlike existing KGQA methods, KARPA avoids stepwise traversal, requires no additional training, and is adaptable to various LLM architectures. Extensive experimental results show that KARPA achieves state-of-the-art performance in KGQA tasks, delivering both high efficiency and accuracy. Our code will be available on Github.