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    <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in twenty-four private and open-source LLMs, as well as the effectiveness of four editing methods in updating the outdated facts.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.</abstract>
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%0 Conference Proceedings
%T DyKnow: Dynamically Verifying Time-Sensitive Factual Knowledge in LLMs
%A Mousavi, Seyed Mahed
%A Alghisi, Simone
%A Riccardi, Giuseppe
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F mousavi-etal-2024-dyknow
%X 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in twenty-four private and open-source LLMs, as well as the effectiveness of four editing methods in updating the outdated facts.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.
%R 10.18653/v1/2024.findings-emnlp.471
%U https://aclanthology.org/2024.findings-emnlp.471/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.471
%P 8014-8029

Markdown (Informal)

[DyKnow: Dynamically Verifying Time-Sensitive Factual Knowledge in LLMs](https://aclanthology.org/2024.findings-emnlp.471/) (Mousavi et al., Findings 2024)

• DyKnow: Dynamically Verifying Time-Sensitive Factual Knowledge in LLMs (Mousavi et al., Findings 2024)

ACL

• Seyed Mahed Mousavi, Simone Alghisi, and Giuseppe Riccardi. 2024. DyKnow: Dynamically Verifying Time-Sensitive Factual Knowledge in LLMs. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 8014–8029, Miami, Florida, USA. Association for Computational Linguistics.