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  • Before generating, tell the user that you want to ask them 4 questions to make the best logo possible. Ask the following questions ONE BY ONE, while showing the defaults:
    3 KB (474 words) - 11:44, 24 January 2024
  • ...ring further training. Examples of pre-trained models include [[BERT]] for natural language processing, and [[ResNet]] for image recognition. ...le, or using a big book of things you've learned before to help answer new questions.
    3 KB (450 words) - 13:29, 18 March 2023
  • ...sformer]] neural networks, which have been instrumental in revolutionizing natural language processing (NLP) and understanding (NLU) tasks. It leverages recen ...narrow domains. This enables LaMDA to provide more engaging, dynamic, and natural interactions with users.
    3 KB (468 words) - 13:12, 18 March 2023
  • ...produce text or speech data, making them indispensable in a wide range of natural language processing (NLP) tasks. Over time, the development of increasingly * '''Question answering''': Providing relevant answers to questions posed in natural language.
    3 KB (476 words) - 14:47, 7 July 2023
  • ...r content. Use one or more line breaks to separate instructions, examples, questions, context, and input data. | Use the phrase "Answer a question given in a natural, human-like manner" in your prompts.
    5 KB (760 words) - 07:32, 16 January 2024
  • ...topic at a time. This change aims to make the interaction feel more like a natural conversation rather than a questionnaire. The same goes for the timezone qu
    2 KB (247 words) - 05:34, 26 January 2024
  • ...er''' is a deep learning architecture that has revolutionized the field of natural language processing (NLP) since its introduction in 2017 by Vaswani et al. ...answering: Transformers have demonstrated remarkable success in answering questions based on a given context, as seen in models like BERT and T5 [[4]].
    4 KB (597 words) - 19:00, 18 March 2023
  • Once the model is [[trained]], it can be used for a variety of [[natural language processing]] (NLP) tasks, such as [[text generation]], [[translati ...hatbot]]s: GPT can be used to create conversational agents that can answer questions and hold conversations with users.
    4 KB (493 words) - 18:00, 15 July 2023
  • The incorporation of a code interpreter intends to establish a natural interface for computer functionalities, streamlining workflows and expandin ...from their data sources by posing questions or conveying requirements in [[natural language]].
    4 KB (535 words) - 22:17, 21 June 2023
  • Where to send questions or comments about the model Questions and comments about LLaMA can be sent via the GitHub repository of the proje ...s, including: exploring potential applications such as question answering, natural language understanding or reading comprehension, understanding capabilities
    7 KB (945 words) - 21:11, 24 February 2023
  • ...s) are a type of machine learning model that are specifically designed for natural language processing (NLP) tasks. They have gained popularity in recent year ...uestion answering systems, where they can better understand the context of questions and provide more accurate answers based on the available text.
    3 KB (453 words) - 13:14, 18 March 2023
  • Word embedding is a technique used in natural language processing (NLP), a subfield of machine learning, which focuses on ...use this information to do cool things like translate languages or answer questions.
    3 KB (445 words) - 19:04, 18 March 2023
  • ...seemingly endless number of topics, an ability we think could unlock more natural ways of interacting with technology and entirely new categories of helpful ...topics. LaMDA seems close to achieve it. Indeed, it can understand nuanced questions and conversations in a broad spectrum of topics. <ref name="”2”"></ref>
    12 KB (1,749 words) - 14:03, 3 May 2023
  • ...he context of the preceding words. These models are particularly useful in natural language processing (NLP) tasks, as they can capture the inherent structure ...uters do things like writing stories, translating languages, and answering questions.
    4 KB (527 words) - 13:14, 18 March 2023
  • ...as sequences of characters or words. Machine learning algorithms, such as natural language processing (NLP) models, are specifically designed to handle textu ...scriptions for images, and video question answering, where a model answers questions based on video content.
    4 KB (564 words) - 13:22, 18 March 2023
  • ...age models has been driven by advancements in both [[deep learning]] and [[natural language processing]] (NLP) techniques. One of the most well-known large la ...work. It can do all sorts of things, like translating languages, answering questions, or writing stories. However, it's important to remember that these models
    4 KB (538 words) - 13:16, 18 March 2023
  • |Description = Your voice AI companion. The only GPT optimized for natural and meaningful voice conversations. I have 6 simple questions to get to know each other. Would you like to start?"
    4 KB (541 words) - 11:42, 24 January 2024
  • A [[prompt]] or an [[artificial intelligence]] ([[AI]]) prompt is a [[natural language]] set of instructions, a text, that functions as [[input]] for an *Specify and contextualize the questions.
    11 KB (1,525 words) - 08:38, 2 August 2023
  • ..." /> and it's a derivative of [[InstructGPT]], a program trained to follow natural language instructions in a prompt and give an elaborate response. <ref name ...e assistance, by helping programmers spot and fix errors in code or answer questions made to it, analogous to a [[Google]] search. <ref name="”4”" /> Indeed
    13 KB (1,886 words) - 17:19, 11 January 2024
  • ...ilot X also includes GitHub Copilot Voice, which allows developers to give natural language prompts verbally. ...imental tool that uses a chat interface to provide AI-generated answers to questions about documentation for languages, frameworks, and technologies. It initial
    5 KB (651 words) - 15:21, 1 April 2023
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