DyKnow: Dynamically Verifying Time-Sensitive Factual Knowledge in LLMs

• URL: http://arxiv.org/abs/2404.08700v3
• Date: Wed, 02 Oct 2024 07:26:40 GMT
• Title: DyKnow: Dynamically Verifying Time-Sensitive Factual Knowledge in LLMs
• Authors: Seyed Mahed Mousavi, Simone Alghisi, Giuseppe Riccardi,
• Abstract summary: We present an approach to dynamically evaluate the knowledge in LLMs and their time-sensitiveness against Wikidata. We evaluate the time-sensitive knowledge in twenty-four private and open-source LLMs, as well as the effectiveness of four editing methods in updating the outdated facts. Our results show that 1) outdatedness is a critical problem across state-of-the-art LLMs; 2) LLMs output inconsistent answers when prompted with slight variations of the question prompt; and 3) the performance of the state-of-the-art knowledge editing algorithms is very limited.
• Score: 1.7764955091415962
• License:
• Abstract: LLMs acquire knowledge from massive data snapshots collected at different timestamps. Their knowledge is then commonly evaluated using static benchmarks. However, factual knowledge is generally subject to time-sensitive changes, and static benchmarks cannot address those cases. We present an approach to dynamically evaluate the knowledge in LLMs and their time-sensitiveness against Wikidata, a publicly available up-to-date knowledge graph. We evaluate the time-sensitive knowledge in twenty-four private and open-source LLMs, as well as the effectiveness of four editing methods in updating the outdated facts. Our results show that 1) outdatedness is a critical problem across state-of-the-art LLMs; 2) LLMs output inconsistent answers when prompted with slight variations of the question prompt; and 3) the performance of the state-of-the-art knowledge editing algorithms is very limited, as they can not reduce the cases of outdatedness and output inconsistency.

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