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There are numerous pre-trained models that support common tasks in different modalities, such as: Natural Language Processing: text classification, named entity recognition, question answering, language modeling, summarization, translation, multiple choice, and text generation.
Question answering systems in the context of [vague] machine reading applications have also been constructed in the medical domain, for instance related to [vague] Alzheimer's disease. [3] Open-domain question answering deals with questions about nearly anything and can only rely on general ontologies and world knowledge. Systems designed for ...
T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI introduced in 2019. [1] [2] Like the original Transformer model, [3] T5 models are encoder-decoder Transformers, where the encoder processes the input text, and the decoder generates the output text.
The XLNet was an autoregressive Transformer designed as an improvement over BERT, with 340M parameters and trained on 33 billion words.It was released on 19 June, 2019, under the Apache 2.0 license. [1]
Flamingo demonstrated the effectiveness of the tokenization method, finetuning a pair of pretrained language model and image encoder to perform better on visual question answering than models trained from scratch. [84] Google PaLM model was fine-tuned into a multimodal model PaLM-E using the tokenization method, and applied to robotic control. [85]
GPT-2's flexibility was described as "impressive" by The Verge; specifically, its ability to translate text between languages, summarize long articles, and answer trivia questions were noted. [ 17 ] A study by the University of Amsterdam employing a modified Turing test found that at least in some scenarios, participants were unable to ...
BigScience Large Open-science Open-access Multilingual Language Model (BLOOM) [1] [2] is a 176-billion-parameter transformer-based autoregressive large language model (LLM). The model, as well as the code base and the data used to train it, are distributed under free licences. [ 3 ]
As of 2024, some of the most powerful language models, such as o1, Gemini and Claude 3, were reported to achieve scores around 90%. [ 4 ] [ 5 ] An expert review of 3,000 randomly sampled questions found that over 9% of the questions are wrong (either the question is not well-defined or the given answer is wrong), which suggests that 90% is ...