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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.
[33] [34] Generative AI planning systems used symbolic AI methods such as state space search and constraint satisfaction and were a "relatively mature" technology by the early 1990s. They were used to generate crisis action plans for military use, [35] process plans for manufacturing [33] and decision plans such as in prototype autonomous ...
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.
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.
Automated journalism, also known as algorithmic journalism or robot journalism, [1] [2] [3] is a term that attempts to describe modern technological processes that have infiltrated the journalistic profession, such as news articles and videos generated by computer programs.
Already in spring 2017, even before the "Attention is all you need" preprint was published, one of the co-authors applied the "decoder-only" variation of the architecture to generate fictitious Wikipedia articles. [32] Transformer architecture is now used in many generative models that contribute to the ongoing AI boom.
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