18 Questions
What is the main advantage of Large Language Models (LLMs)?
They can understand, generate, and process human language proficiently.
Which type of model is ideal for a wide variety of different tasks?
Foundation models
What is the primary need for some Generative AI projects according to the text?
Customization and development using low code tools
What is a key characteristic of Task-specific models?
They can only perform one specific task.
Which element is crucial for enabling more complex use cases in Generative AI projects?
Development frameworks
What differentiates Large Language Models from traditional machine learning algorithms?
Their ability to generate content using very large datasets.
What is the key difference between Open-Source and Proprietary Large Language Models (LLMs)?
Open-Source models are usually smaller in size compared to Proprietary models.
Which characteristic best describes the Main/Popular Open-Source LLM, Llama 2?
Fine-tuned with supervised instruction from UC Berkeley researchers.
What is the purpose of prompt engineering in the context of generative AI models?
To guide the output of language models for desired responses
What distinguishes Vicuna from other Main/Popular Open-Source LLMs mentioned in the text?
Fine-tuned from Llama 2 with supervised instruction.
What is a common disadvantage associated with Foundational Models as mentioned in the text?
They lack up-to-date data and domain-specific knowledge.
Which technique involves providing inputs in the form of text or images to confine the set of responses a generative AI model can produce?
Prompt Engineering
Why are Proprietary LLMs generally larger in size compared to Open-Source LLMs?
Proprietary models have access to more diverse training data sources.
Which approach involves designing effective instructions or queries to influence the generation of content in AI systems?
Prompt Engineering
Which company owns the Main/Popular Proprietary LLM Gemini?
In the landscape of large language models, what is the main goal of prompt engineering?
To control and optimize model responses for desired outcomes
What differentiates prompt engineering from updating a model's parameters in generative AI?
Prompt engineering guides output without changing parameters.
Which technique involves providing inputs to specify and control the set of responses a generative AI model can produce?
Prompt Engineering
Study Notes
Generative AI: Current Stack and Applications
- Generative AI has enabled thousands of business opportunities due to AI democratization
- Prompt techniques and low-code/no-code tools have made it possible for projects to require little to no programming
- Development frameworks enable more complex use cases
Large Language Models (LLMs)
- LLMs are deep learning algorithms that can recognize, summarize, translate, predict, and generate content using large datasets
- They are sophisticated AI systems that can understand, generate, and process human language with high proficiency
Task-Specific vs. Foundation Models
- Task-specific models are trained for a single task, whereas foundation models can be used for a wide variety of tasks
- Foundation models are generally trained in an unsupervised manner using huge amounts of unstructured data
Landscape of Large Language Models
- The world of LLMs is divided between open-source models (open access) and proprietary models (closed-source models)
- Proprietary models are owned by companies and have license restrictions, whereas open-source models are free to access and modify
Open-Source Large Language Models
- Llama 2 is a popular open-source LLM provided by Meta, with pre-trained and fine-tuned models containing 7-70 billion parameters
- Vicuna is another open-source LLM, fine-tuned from Llama 2 with supervised instruction fine-tuning
- Bloom is a multilanguage model with 176 billion parameters, provided by BigScience
Proprietary Large Language Models
- Examples include OpenAI's GPT and Google's Gemini, which are larger (over 175 billion parameters) and closed-source
Disadvantages of Foundational Models
- They can perform poorly on specific use cases, have knowledge cut-offs, and lack domain-specific knowledge
- Training data can include biased or incorrect information, and they can generate harmful content if not properly controlled
Methods to Improve Foundational Models
- Reinforced Learning from Human Feedback (RLHF)
- Supervised Fine-Tuning (SFT)
- Retrieval-Augmented Generation (RAG)
Prompt Engineering
- Prompt engineering is the discipline of providing inputs to generative AI models to specify and confine the set of responses
- It involves designing effective instructions or queries to guide the output of language models, optimizing their responses for desired results
Explore the world of Large Language Models (LLMs) and understand the division between Open-Source Models and Proprietary Models. Learn about the features, ownership, and restrictions associated with each type of model.
Make Your Own Quizzes and Flashcards
Convert your notes into interactive study material.
Get started for free
