EvoWiki: Evaluating LLMs on Evolving Knowledge

• URL: http://arxiv.org/abs/2412.13582v1
• Date: Wed, 18 Dec 2024 08:04:57 GMT
• Title: EvoWiki: Evaluating LLMs on Evolving Knowledge
• Authors: Wei Tang, Yixin Cao, Yang Deng, Jiahao Ying, Bo Wang, Yizhe Yang, Yuyue Zhao, Qi Zhang, Xuanjing Huang, Yugang Jiang, Yong Liao,
• Abstract summary: EvoWiki is an evolving dataset designed to reflect knowledge evolution by categorizing information into stable, evolved, and uncharted states. Our results indicate that current models often struggle with evolved knowledge, frequently providing outdated or incorrect responses. EvoWiki provides a robust benchmark for advancing future research on the knowledge evolution capabilities of large language models.
• Score: 72.92365627254063
• License:
• Abstract: Knowledge utilization is a critical aspect of LLMs, and understanding how they adapt to evolving knowledge is essential for their effective deployment. However, existing benchmarks are predominantly static, failing to capture the evolving nature of LLMs and knowledge, leading to inaccuracies and vulnerabilities such as contamination. In this paper, we introduce EvoWiki, an evolving dataset designed to reflect knowledge evolution by categorizing information into stable, evolved, and uncharted states. EvoWiki is fully auto-updatable, enabling precise evaluation of continuously changing knowledge and newly released LLMs. Through experiments with Retrieval-Augmented Generation (RAG) and Contunual Learning (CL), we evaluate how effectively LLMs adapt to evolving knowledge. Our results indicate that current models often struggle with evolved knowledge, frequently providing outdated or incorrect responses. Moreover, the dataset highlights a synergistic effect between RAG and CL, demonstrating their potential to better adapt to evolving knowledge. EvoWiki provides a robust benchmark for advancing future research on the knowledge evolution capabilities of large language models.

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