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The most well-known bot that fights vandalism is ClueBot NG. The bot was created by Wikipedia users Christopher Breneman and Naomi Amethyst in 2010 (succeeding the original ClueBot created in 2007; NG stands for Next Generation) [7] and uses machine learning and Bayesian statistics to determine if an edit is vandalism.
Based on the results, AI models seem to be a very powerful brainstorming tool, and via prompt engineering, these AI do allow an impressive amount of refinement to the plan. AI can also be a great tool as a pointer to potential sources and can remind editors of Wikipedia's content policy (NPOV, RS, etc.)
The adoption of generative AI tools led to an explosion of AI-generated content across multiple domains. A study from University College London estimated that in 2023, more than 60,000 scholarly articles—over 1% of all publications—were likely written with LLM assistance. [182]
This may be directly involved with creation of text content, or in support roles related to evaluating article quality, adding metadata, or generating images. As with any machine-generated content, care must be used when employing AI at scale or in applying it where the community consensus is to exercise more caution.
Retrieval-Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data.
To identify AI-generated images and ensure appropriate usage. To help and keep track of AI-using editors who may not realize their deficiencies as a writing tool; The purpose of this project is not to restrict or ban the use of AI in articles, but to verify that its output is acceptable and constructive, and to fix or remove it otherwise.
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