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Questions and Answers
What is a primary benefit of Retrieval Augmented Generation (RAG) in contrast to traditional learning methods?
Which of the following describes a limitation of large language models (LLM)?
What method involves training a pre-trained model on a specific, smaller dataset?
Which technique is used in RAG to prepare data for model input?
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Which of the following is NOT a challenge faced by large language models?
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What is a primary benefit of using the Self Attention mechanism in Transformers?
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Which of the following reflects a limitation of generative AI?
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What role do prompts play in the functioning of language models?
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In the context of large language models, what does autoregressive modeling primarily depend on?
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What characteristic of training data can significantly affect the outputs of generative models?
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Study Notes
Large Language Models
- LLMs generate data sequentially based on conditional probabilities.
- Transformers, a type of neural network, are used as the core of many LLMs.
- Transformers can capture long-range context through self-attention mechanisms.
- LLMs are able to generate text that is contextually relevant, tailored to specific tasks or conditions, thanks to Transformers.
Evolution of LLMs
- LLMs have progressed in terms of size and capability.
Generative AI Applications
- LLMs can be used for a wide range of applications.
- For instance, they can assist in customer service, research, legal discovery, and financial analysis.
Generative AI Limitations
- LLMs can lack control over specific attributes or features of the generated output.
- If training data is biased, limited in scope, or contains errors, these shortcomings may be reflected in the generated output.
- The use of LLMs raises ethical concerns as generated content could be misused.
Prompt Engineering
- Prompts are instructions and context passed to a language model to achieve a desired task.
Retrieval Augmented Generation (RAG)
- RAG combines the capabilities of LLMs with external information.
- It's similar to an open-book exam, allowing the LLM to access and leverage external knowledge.
- RAG involves a five-step process:
- Data Collection and Extraction
- Data Chunking
- Document Embeddings
- Storing Embeddings in a Vectorstore
- Retrieval
- Generation
Data Collection and Extraction
- RAG collects data from various sources like PDFs, websites, databases, and manuals.
Data Chunking
- Data is split into smaller, more manageable chunks to focus on specific topics.
Embeddings
- Words and sentences are represented numerically as vectors to capture semantic meaning.
Document Embeddings
- Splitted documents are transformed into embeddings, which help the system understand user queries and match them with relevant chunks.
Store Embeddings in Vectorstore
- Embeddings are stored in a vectorstore, a specialized database that allows for quick search and retrieval of similar vectors.
Retrieval
- User queries are transformed into embeddings and compared against embeddings in the vectorstore to identify the most relevant chunks.
Generation
- The LLM generates the answer based on user's query, retrieved context, and system instructions.
Applications of RAG
- Customer Services: RAG-powered chatbots can leverage knowledge bases and customer history to provide personalized support.
- Research Market Intelligence: RAG helps to quickly synthesize insights from large volumes of data.
- Legal Discovery: RAG helps legal professionals quickly find relevant precedents and arguments across large collections of case law.
- Financial Analysis: RAG can ingest earnings statements, press releases, and regulatory filings to generate investment insights and trading signals.
Advanced RAG Techniques
- Parent Document Retriever: RAG systems can be built with hierarchical structures.
- Hybrid Fusion Search: Combines keyword search and semantic search for more comprehensive results.
- Contextual Compressor: This technique helps deliver personalized experiences based on user preferences.
Customized AI Author Agents
- Personalized AI authors can assist with tasks like co-authoring emails and preparing for meetings.
- AI authors can utilize previous work to generate new content tailored to specific audiences and needs.
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Description
This quiz explores the fundamentals of Large Language Models (LLMs), including their underlying technology, transformer architecture, and applications in various fields. It also addresses the evolution of LLMs, their limitations, and the ethical concerns surrounding their use. Test your knowledge on how LLMs generate text and their impact on society.
