Wikipedia in the Era of LLMs: Evolution and Risks
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.
67c7c42269d99dd25c5ba0ce
Siming Huang
claimed_verified
2025-03-05T10:11:32.237Z
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Siming Huang
user
hsm316
67c7c42269d99dd25c5ba0cf
Yuliang Xu
admin_assigned
2025-03-05T10:39:56.806Z
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Yuliang Xu
user
sdzzxyl
67c7c42269d99dd25c5ba0d0
Mingmeng Geng
extracted_confirmed
2025-03-05T13:45:31.195Z
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Mingmeng Geng
user
mgeng
67c7c42269d99dd25c5ba0d1
Yao Wan
67c7c42269d99dd25c5ba0d2
Dongping Chen
admin_assigned
2025-03-05T10:40:02.647Z
65e2be1e630e2db23829ee8d
/avatars/294f9ba909037f03669dc0bb80cabfe3.svg
Dongping Chen
user
fjchendp
Wikipedia in the Era of LLMs: Evolution and Risks
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.
The analysis of Large Language Models' impact on Wikipedia shows a modest influence on article content and NLP tasks like machine translation and RAG, highlighting potential future risks.
Large Language Models (LLMs) retrieval-augmented generation (RAG) machine translation benchmark