  While large language models (LLMs) have shown remarkable capabilities in
natural language processing, they struggle with complex, multi-step reasoning
tasks involving knowledge graphs (KGs). Existing approaches that integrate LLMs
and KGs either underutilize the reasoning abilities of LLMs or suffer from
prohibitive computational costs due to tight coupling. To address these
limitations, we propose a novel collaborative framework named EffiQA that can
strike a balance between performance and efficiency via an iterative paradigm.
EffiQA consists of three stages: global planning, efficient KG exploration, and
self-reflection. Specifically, EffiQA leverages the commonsense capability of
LLMs to explore potential reasoning pathways through global planning. Then, it
offloads semantic pruning to a small plug-in model for efficient KG
exploration. Finally, the exploration results are fed to LLMs for
self-reflection to further improve the global planning and efficient KG
exploration. Empirical evidence on multiple KBQA benchmarks shows EffiQA's
effectiveness, achieving an optimal balance between reasoning accuracy and
computational costs. We hope the proposed new framework will pave the way for
efficient, knowledge-intensive querying by redefining the integration of LLMs
and KGs, fostering future research on knowledge-based question answering.
