Authors: Siming Huang, Yuliang Xu, Mingmeng Geng, Yao Wan, Dongping Chen
Abstract: In this paper, we present a thorough analysis of the impact of Large Language
Models (LLMs) on Wikipedia, examining the evolution of Wikipedia through
existing data and using simulations to explore potential risks. We begin by
analyzing page views and article content to study Wikipedia’s recent changes
and assess the impact of LLMs. Subsequently, we evaluate how LLMs affect
various Natural Language Processing (NLP) tasks related to Wikipedia, including
machine translation and retrieval-augmented generation (RAG). Our findings and
simulation results reveal that Wikipedia articles have been influenced by LLMs,
with an impact of approximately 1%-2% in certain categories. If the machine
translation benchmark based on Wikipedia is influenced by LLMs, the scores of
the models may become inflated, and the comparative results among models might
shift as well. Moreover, the effectiveness of RAG might decrease if the
knowledge base becomes polluted by LLM-generated content. While LLMs have not
yet fully changed Wikipedia’s language and knowledge structures, we believe
that our empirical findings signal the need for careful consideration of
potential future risks.
Source: http://arxiv.org/abs/2503.02879v1
