LiveSearchBench: An Automatically Constructed Benchmark for Retrieval and Reasoning over Dynamic Knowledge

• URL: http://arxiv.org/abs/2511.01409v2
• Date: Thu, 06 Nov 2025 15:57:51 GMT
• Title: LiveSearchBench: An Automatically Constructed Benchmark for Retrieval and Reasoning over Dynamic Knowledge
• Authors: Heng Zhou, Ao Yu, Yuchen Fan, Jianing Shi, Li Kang, Hejia Geng, Yongting Zhang, Yutao Fan, Yuhao Wu, Tiancheng He, Yiran Qin, Lei Bai, Zhenfei Yin,
• Abstract summary: We present LiveSearchBench, an automated pipeline for constructing retrieval-dependent benchmarks from recent knowledge updates.<n>Our method computes deltas between successive Wikidata snapshots, filters candidate triples for quality, and synthesizes natural-language questions at three levels of reasoning difficulty.<n> Experiments show a pronounced performance drop when models confront facts that post-date pretraining, with the gap most salient on multi-hop queries.
• Score: 31.40589987269264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Evaluating large language models (LLMs) on question answering often relies on static benchmarks that reward memorization and understate the role of retrieval, failing to capture the dynamic nature of world knowledge. We present LiveSearchBench, an automated pipeline for constructing retrieval-dependent benchmarks from recent knowledge updates. Our method computes deltas between successive Wikidata snapshots, filters candidate triples for quality, and synthesizes natural-language questions at three levels of reasoning difficulty, each guaranteed to admit a unique, verifiable answer through SPARQL validation. The pipeline is fully automated, scalable across time, and minimizes human intervention, enabling continual regeneration of temporally grounded benchmarks. Experiments show a pronounced performance drop when models confront facts that post-date pretraining, with the gap most salient on multi-hop queries. Retrieval augmented methods and larger, instruction-tuned models provide partial gains but fail to close this recency gap. By design, LiveSearchBench shifts evaluation from static memorization toward tasks that require up-to-date retrieval and reasoning, offering a foundation for systematic, long-term assessment of LLMs under evolving knowledge.

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