![The Evolution: From RNNs to Transformers
As she said this, she looked down at her hands...
['As', 'she', 'said', 'this', ',', 'she', 'looked', 'down', 'at', 'her', 'hands',
'...']
Encoding
Tokenization
Embeddings
Context Vector
Autoregressive
Generation
Example:](https://image.slidesharecdn.com/largelanguagemodel-240809082244-29b76dac/85/Large-Language-Models-How-Large-Language-Models-Work-Introduction-to-LLM-Simplilearn-3-320.jpg)
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Large Language Models | How Large Language Models Work? | Introduction to LLM | Simplilearn
The document discusses the evolution of artificial intelligence from recurrent neural networks (RNNs) to transformers, highlighting the advantages of transformers like their attention mechanism. It explains large language models (LLMs), which leverage deep learning to generate and understand text, and outlines their training methods and various applications. Additionally, it addresses the benefits and challenges associated with LLMs, such as operational costs and ethical concerns.
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Large Language Models | How Large Language Models Work? | Introduction to LLM | Simplilearn
- 2. The Evolution: FromRNNs to Transformers 2017 Unlike RNNs, transformers use an attention mechanism, allowing them to consider the entire sentence or paragraph simultaneously, rather than one word at a time Transformer
- 3. The Evolution: FromRNNs to Transformers As she said this, she looked down at her hands... ['As', 'she', 'said', 'this', ',', 'she', 'looked', 'down', 'at', 'her', 'hands', '...'] Encoding Tokenization Embeddings Context Vector Autoregressive Generation Example:
- 4. Why Transformers CanPredict Text Redundancy in Language Entropy and Predictability Why Predicting Text is Possible:
- 5. What is aLarge Language Model? A Large Language Model is a type of artificial intelligence that uses deep learning and vast datasets to understand, summarize, generate, and predict new content. LLMs are a subset of generative AI, specifically designed to create text-based content GPT-3 Megatron-Turing NLG 530B Examples:
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- 8. How Do LargeLanguage Models Work? LLMs are trained using unsupervised learning, which means they learn from large datasets without labeled data. They find patterns and relationships in the data, enabling them to perform various tasks like text generation, summarization, and translation
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- 11. Types of LargeLanguage Models Decoder-Only Models Encoder-Only Models 1 2 Encoder- Decoder Models 3
- 12. Advantages and Challengesof Large Language Models Flexibility Extensibility Performance Accuracy Ease of Training Advantages Efficiency Operational Costs Development Costs Bias Ethical Concerns Explainability Challenges Hallucination