When Facts Change: Probing LLMs on Evolving Knowledge with evolveQA

• URL: http://arxiv.org/abs/2510.19172v1
• Date: Wed, 22 Oct 2025 02:12:32 GMT
• Title: When Facts Change: Probing LLMs on Evolving Knowledge with evolveQA
• Authors: Nishanth Sridhar Nakshatri, Shamik Roy, Manoj Ghuhan Arivazhagan, Hanhan Zhou, Vinayshekhar Bannihatti Kumar, Rashmi Gangadharaiah,
• Abstract summary: We introduce evolveQA, a benchmark specifically designed to evaluate LLMs on temporally evolving knowledge.<n>Our framework identifies naturally occurring knowledge evolution and generates questions with gold answers tailored to different LLM knowledge cut-off dates.<n>We demonstrate significant performance drops of up to 31% on evolveQA compared to static knowledge questions.
• Score: 11.701030951844222
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: LLMs often fail to handle temporal knowledge conflicts--contradictions arising when facts evolve over time within their training data. Existing studies evaluate this phenomenon through benchmarks built on structured knowledge bases like Wikidata, but they focus on widely-covered, easily-memorized popular entities and lack the dynamic structure needed to fairly evaluate LLMs with different knowledge cut-off dates. We introduce evolveQA, a benchmark specifically designed to evaluate LLMs on temporally evolving knowledge, constructed from 3 real-world, time-stamped corpora: AWS updates, Azure changes, and WHO disease outbreak reports. Our framework identifies naturally occurring knowledge evolution and generates questions with gold answers tailored to different LLM knowledge cut-off dates. Through extensive evaluation of 12 open and closed-source LLMs across 3 knowledge probing formats, we demonstrate significant performance drops of up to 31% on evolveQA compared to static knowledge questions.

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