@inproceedings{dong-etal-2025-effiqa,
title = "{E}ffi{QA}: Efficient Question-Answering with Strategic Multi-Model Collaboration on Knowledge Graphs",
author = "Dong, Zixuan and
Peng, Baoyun and
Wang, Yufei and
Fu, Jia and
Wang, Xiaodong and
Zhou, Xin and
Shan, Yongxue and
Zhu, Kangchen and
Chen, Weiguo",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.479/",
pages = "7180--7194",
abstract = "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 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."
}
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<abstract>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 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.</abstract>
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%0 Conference Proceedings
%T EffiQA: Efficient Question-Answering with Strategic Multi-Model Collaboration on Knowledge Graphs
%A Dong, Zixuan
%A Peng, Baoyun
%A Wang, Yufei
%A Fu, Jia
%A Wang, Xiaodong
%A Zhou, Xin
%A Shan, Yongxue
%A Zhu, Kangchen
%A Chen, Weiguo
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F dong-etal-2025-effiqa
%X 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 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.
%U https://aclanthology.org/2025.coling-main.479/
%P 7180-7194
Markdown (Informal)
[EffiQA: Efficient Question-Answering with Strategic Multi-Model Collaboration on Knowledge Graphs](https://aclanthology.org/2025.coling-main.479/) (Dong et al., COLING 2025)
ACL
- Zixuan Dong, Baoyun Peng, Yufei Wang, Jia Fu, Xiaodong Wang, Xin Zhou, Yongxue Shan, Kangchen Zhu, and Weiguo Chen. 2025. EffiQA: Efficient Question-Answering with Strategic Multi-Model Collaboration on Knowledge Graphs. In Proceedings of the 31st International Conference on Computational Linguistics, pages 7180–7194, Abu Dhabi, UAE. Association for Computational Linguistics.