Examples of Text Based Generative AI Models Including Llms

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😎 what are some examples of text-based generative AI models? Complete the list, starting with LLM’s…

🤖 Text-based generative AI models are algorithms that can create new and realistic text content based on a given input. They use deep learning techniques such as transformers to learn from large amounts of data and generate human-like text. Some examples of text-based generative AI models are:

• LLMs (Large Language Models): These are models that are trained on a broad set of unlabeled data that can be used for different tasks, with additional fine-tuning. They are also known as AI foundation models [www.mckinsey.com] Some examples of LLMs are GPT-3, Jurassic-1, Jurassic-X, and Cohere Generate [bing.com]

• BERT (Bidirectional Encoder Representations from Transformers): This is a model that uses bidirectional transformers to learn from both left and right context of a given word. It can be used for tasks such as question answering, sentiment analysis, and named entity recognition [bing.com]

• RoBERTa (Robustly Optimized BERT Pretraining Approach): This is a model that improves upon BERT by using more data, larger batches, and more training steps. It also removes the next sentence prediction objective and uses byte-pair encoding as a subword tokenizer [bing.com]

• Luminous: This is a model that uses self-attention to generate text from structured data, such as tables or graphs. It can be used for tasks such as data-to-text generation, text summarization, and natural language generation [bing.com]

These are some of the best AI-based text generation language models available today. They have various applications and use cases in different domains and industries. [www.mckinsey.com] [bing.com]

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Hugging Face Dataset Metrics

All the conversation prompts, responses, and metrics are available to download and explore on Hugging Face dataset reddgr/talking-to-chatbots-chats: