Wikipedia in the Age of Large Language Models: Opportunities and Challenges

Large language models (LLMs) have revolutionized the world of information processing and are increasingly influencing established knowledge resources like Wikipedia. This article examines the complex interactions between LLMs and Wikipedia, investigates the impacts to date, and analyzes potential risks and opportunities.

The Influence of LLMs on Wikipedia

Studies show that the influence of LLMs on Wikipedia content is already measurable. Estimates suggest an influence of about 1-2% in certain categories. This is evident, for example, in the change of language style and phrasing in some articles. The increasing use of LLMs for text generation holds the potential to accelerate and simplify the creation and editing of Wikipedia articles. At the same time, this raises questions about the quality, objectivity, and verifiability of the content.

Impact on NLP Tasks

The influence of LLMs on Wikipedia also has implications for various natural language processing (NLP) tasks. For example, machine translation benchmarks based on Wikipedia data can be skewed by LLM-generated content. This potentially leads to inflated evaluations of translation models and distorts the comparison between different models. Retrieval-Augmented Generation (RAG), a technique that uses external knowledge sources like Wikipedia, can also be impaired in its effectiveness if the quality of the knowledge base decreases due to LLM-generated content.

Potential Risks and Opportunities

The increasing prevalence of LLMs presents both opportunities and risks for Wikipedia. Among the opportunities is the possibility of accelerating the creation and updating of articles and lowering the barrier to contributing to Wikipedia. LLMs could, for example, be used to generate draft articles, which are then reviewed and revised by human authors. This could help to close gaps in the encyclopedia and improve the coverage of niche topics.

At the same time, there are significant risks. The automatic generation of content by LLMs can lead to a reduction in the quality and objectivity of the articles. LLMs tend to amplify existing biases in the training data and can also invent or distort facts. This poses a threat to the credibility of Wikipedia.

Another risk is the possibility that LLMs are deliberately used to flood Wikipedia with misinformation or to manipulate public opinion. Therefore, it is important to develop mechanisms to identify and evaluate LLM-generated content.

Outlook

Although LLMs have the potential to enrich and improve Wikipedia, it is crucial to take the associated risks seriously. The future development of Wikipedia in the age of LLMs depends crucially on whether it is possible to leverage the opportunities of this technology while minimizing the risks. This requires close collaboration between the Wikipedia community, developers of LLMs, and experts in the fields of NLP and information ethics.

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