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Mattingly "Text and Data Mining: Building Data Driven Applications"
The document discusses key concepts in data-driven applications, focusing on large language models (LLMs) like GPT-4, which are designed for natural language understanding and generation through extensive training. It introduces retrieval-augmented generation (RAG), a method that enhances LLMs by utilizing vector databases to reduce inaccuracies in data generation. Additionally, it addresses the limitations and ethical considerations associated with LLMs, including hallucinations and biases.
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Mattingly "Text and Data Mining: Building Data Driven Applications"
- 1. Text and DataMining Building Data-Driven Apps
- 2. 1. Refresher onVector Databases 2. Large Language Models 3. Retrieval-Augmented Generation (RAG) 4. Streamlit 5. Gradio 6. R Shiny Goals
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- 5. Large Language Models What arethey? ● Large language models (LLMs) like GPT-4 are advanced AI systems trained on extensive datasets to understand and generate human-like text.
- 6. Large Language Models What arethey? ● Natural Language Understanding (NLU) ● Natural Language Generation (NLG)
- 7. Large Language Models What arethey? ● Natural Language Understanding (NLU) ● Natural Language Generation (NLG)
- 8. Large Language Models Training ● LLMsare trained on vast amounts of text data from the internet, books, and other sources. This training enables them to learn language patterns, context, and various knowledge domains.
- 9. Large Language Models Where dothey excel? ● Generating new data based on their knowledge of existing data. ○ Code ○ Essays ○ Images
- 10. Large Language Models Limitations ● Hallucinations- Generating incorrect data ● Ethics and Biases ● Copyright Infringement
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- 12. How tall isWookie?
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- 15. RAG What is it? ●RAG allows for you to combine the strengths of large language models (LLMs) with vector databases ● It limits the chances for an LLM to hallucinate (generate fake information) ● It uses a vector database to find relevant material to a query
- 16. RAG What is it? ●RAG allows for you to combine the strengths of large language models (LLMs) with vector databases ● It limits the chances for an LLM to hallucinate (generate fake information) ● It uses a vector database to find relevant material to a query 1 2 3 4 5 6