Diagnosing and Addressing Pitfalls in KG-RAG Datasets: Toward More Reliable Benchmarking

• URL: http://arxiv.org/abs/2505.23495v1
• Date: Thu, 29 May 2025 14:44:52 GMT
• Title: Diagnosing and Addressing Pitfalls in KG-RAG Datasets: Toward More Reliable Benchmarking
• Authors: Liangliang Zhang, Zhuorui Jiang, Hongliang Chi, Haoyang Chen, Mohammed Elkoumy, Fali Wang, Qiong Wu, Zhengyi Zhou, Shirui Pan, Suhang Wang, Yao Ma,
• Abstract summary: Knowledge Graph Question Answering systems rely on high-quality benchmarks to evaluate complex multi-hop reasoning.<n>Despite their widespread use, popular datasets such as WebQSP and CWQ suffer from critical quality issues.<n>We introduce KGQAGen, an LLM-in-the-loop framework that systematically resolves these pitfalls.<n>Our findings advocate for more rigorous benchmark construction and position KGQAGen as a scalable framework for advancing KGQA evaluation.
• Score: 56.27361644734853
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge Graph Question Answering (KGQA) systems rely on high-quality benchmarks to evaluate complex multi-hop reasoning. However, despite their widespread use, popular datasets such as WebQSP and CWQ suffer from critical quality issues, including inaccurate or incomplete ground-truth annotations, poorly constructed questions that are ambiguous, trivial, or unanswerable, and outdated or inconsistent knowledge. Through a manual audit of 16 popular KGQA datasets, including WebQSP and CWQ, we find that the average factual correctness rate is only 57 %. To address these issues, we introduce KGQAGen, an LLM-in-the-loop framework that systematically resolves these pitfalls. KGQAGen combines structured knowledge grounding, LLM-guided generation, and symbolic verification to produce challenging and verifiable QA instances. Using KGQAGen, we construct KGQAGen-10k, a ten-thousand scale benchmark grounded in Wikidata, and evaluate a diverse set of KG-RAG models. Experimental results demonstrate that even state-of-the-art systems struggle on this benchmark, highlighting its ability to expose limitations of existing models. Our findings advocate for more rigorous benchmark construction and position KGQAGen as a scalable framework for advancing KGQA evaluation.

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