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Retrieval-augmented generation (RAG) is a technique that enables generative artificial intelligence (Gen AI) models to retrieve and incorporate new information. [1] 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 supplement information from its pre-existing training data.
If they used 20 to make lunch and bought 6 more, how many apples do they have?", Google claims that a CoT prompt might induce the LLM to answer "A: The cafeteria had 23 apples originally. They used 20 to make lunch. So they had 23 - 20 = 3. They bought 6 more apples, so they have 3 + 6 = 9. The answer is 9."
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File:Python Programming eBook Reader.pdf. ... The LaTeX source code is attached to the PDF file (see imprint). ... Version of PDF format: 1.4:
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The expression was popular in the early days of computing. The first known use is in a 1957 syndicated newspaper article about US Army mathematicians and their work with early computers, [4] in which an Army Specialist named William D. Mellin explained that computers cannot think for themselves, and that "sloppily programmed" inputs inevitably lead to incorrect outputs.
According to John McMillian, author of the 2011 book Smoking Typewriters, The Rag "was a spirited, quirky, and humorous paper, whose founders pushed the New Left's political agenda even as they embraced the counterculture's zeal for rock music, psychedelics, and personal liberation," [7] and, according to historian Douglas Rossinow, the paper ...