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Semantic Scholar is a research tool for scientific literature. It is developed at the Allen Institute for AI and was publicly released in November 2015. [2] Semantic Scholar uses modern techniques in natural language processing to support the research process, for example by providing automatically generated summaries of scholarly papers. [3]
Thankfully, researchers at the Allen Institute for Artificial Intelligence have developed a new model to summarize text from scientific papers, and present it in a few sentences in the form of TL ...
Image collection summarization is another application example of automatic summarization. It consists in selecting a representative set of images from a larger set of images. [13] A summary in this context is useful to show the most representative images of results in an image collection exploration system. Video summarization is a related ...
7.0.3 [6] Free / Online storage free up to 300 MB / Additional storage space available Yes AGPL: Multi-platform desktop version with connectors for Firefox, Chrome and Safari. Web-based access to reference library also available through Zotero.org or through a personal cloud-based database folder on a user's computer (Google Drive, Dropbox, etc.).
Perplexity AI is a conversational search engine that uses large language models (LLMs) to answer queries using sources from the web and cites links within the text response. [3] Its developer, Perplexity AI, Inc., is based in San Francisco, California .
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 alongside many generative models that contribute to the ongoing AI boom.
The citation generation tool of the Visual Editor (WP:REFVISUAL) can also be used when editing the article source, for users who have enabled the 2017 wikitext editor in their preferences. Template:Ref info, which can aid evaluating what kind of citation style was used to write the article; Based on Citoid: Cite templates in Visual Editor
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
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