title: Wikipedia in the Era of LLMs: Evolution and Risks
summary: 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.
paper_authors._id: 67c7c42269d99dd25c5ba0ce
paper_authors.name: Siming Huang
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paper_authors.fullname: Siming Huang
paper_authors.type: user
paper_authors.user: hsm316
paper_authors._id: 67c7c42269d99dd25c5ba0cf
paper_authors.name: Yuliang Xu
paper_authors.status: admin_assigned
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paper_authors._id: 66451b129a4de258b5a028b8
paper_authors.avatarUrl: /avatars/bfa5903aa90d3071fac5c3c14cb1f115.svg
paper_authors.fullname: Yuliang Xu
paper_authors.type: user
paper_authors.user: sdzzxyl
paper_authors._id: 67c7c42269d99dd25c5ba0d0
paper_authors.name: Mingmeng Geng
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-03-05T13:45:31.195Z
paper_authors._id: 67890323f8796231c857231e
paper_authors.avatarUrl: /avatars/f5ccd5186968d880fee9c36324a5f713.svg
paper_authors.fullname: Mingmeng Geng
paper_authors.type: user
paper_authors.user: mgeng
paper_authors._id: 67c7c42269d99dd25c5ba0d1
paper_authors.name: Yao Wan
paper_authors._id: 67c7c42269d99dd25c5ba0d2
paper_authors.name: Dongping Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-05T10:40:02.647Z
paper_authors._id: 65e2be1e630e2db23829ee8d
paper_authors.avatarUrl: /avatars/294f9ba909037f03669dc0bb80cabfe3.svg
paper_authors.fullname: Dongping Chen
paper_authors.type: user
paper_authors.user: fjchendp
paper_title: Wikipedia in the Era of LLMs: Evolution and Risks
paper_summary: 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.
paper_ai_keywords: Large Language Models (LLMs) Wikipedia page views article content simulations Natural Language Processing (NLP) machine translation retrieval-augmented generation (RAG) impact on Wikipedia comparative results knowledge base LLM-generated content