Reading List.md

Readings on Large Language Models and Generative AI

Large Language Models:

1. Introduction to Large Language Models

   Dive into the fundamentals of LLMs, exploring their pre-training and fine-tuning for specific applications.

2. Language Models are Few-Shot Learners

   Delve into the research paper discussing how language models become few-shot learners, showcasing their adaptability.

3. Getting Started with LangChain + Vertex AI PaLM API

   A practical guide to using LangChain and the Vertex AI PaLM API for seamless integration.

4. Learn about LLMs, PaLM models, and Vertex AI

   Explore the capabilities of LLMs, PaLM models, and Vertex AI in this comprehensive guide.

5. Building AI-powered apps on Google Cloud databases using pgvector, LLMs, and LangChain

   Discover how to integrate LLMs and LangChain with Google Cloud databases for AI-powered applications.

6. Training Large Language Models on Google Cloud

   Get hands-on with training large language models on Google Cloud using the provided pipeline examples.

7. Prompt Engineering for Generative AI

   Understand the importance of prompt engineering in the context of Generative AI development.

8. PaLM-E: An embodied multimodal language model

   Explore PaLM-E, an embodied multimodal language model, in this informative blog post from Google AI.

9. Parameter-efficient fine-tuning of large-scale pre-trained language models

   Delve into the nature of parameter-efficient fine-tuning for large-scale pre-trained language models.

10. Understanding Parameter-Efficient LLM Finetuning: Prompt Tuning And Prefix Tuning

    Explore the nuances of parameter-efficient fine-tuning, focusing on prompt tuning and prefix tuning.

11. Parameter-Efficient Fine-Tuning of Large Language Models with LoRA and QLoRA

    Learn about LoRA and QLoRA, techniques for parameter-efficient fine-tuning of large language models.

12. Solving a machine-learning mystery

    Explore the MIT News article discussing large language models in the context of learning mysteries.

Generative AI:

1. Background: What is a Generative Model?

   A foundational exploration of what constitutes a generative model and its implications.

2. Gen AI for Developers

   An in-depth resource for developers diving into the world of Generative AI on Google Cloud.

3. Ask a Techspert: What is generative AI?

   An interview-style piece demystifying generative AI, straight from the techsperts at Google.

4. What is generative AI?

   Get insights from McKinsey on what generative AI is and its potential impact.

5. Building the most open and innovative AI ecosystem

   Explore Google Cloud's approach to building an open and innovative generative AI partner ecosystem.

6. Generative AI is here. Who Should Control It?

   Listen to The New York Times podcast discussing the arrival of generative AI and the question of control.

7. Stanford U & Google’s Generative Agents Produce Believable Proxies of Human Behaviors

   Explore the collaboration between Stanford University and Google in producing believable proxies of human behaviors through generative agents.

8. Generative AI: Perspectives from Stanford HAI

   Read perspectives on generative AI from Stanford University's Human-Centered AI group.

9. Generative AI at Work

   Explore the working paper on generative AI and its applications in various domains.

10. The future of generative AI is niche, not generalized

    Discover insights into the future trajectory of generative AI, emphasizing niche applications over generalized use.

11. The implications of Generative AI for businesses

    Delve into Deloitte's exploration of the implications of generative AI for businesses.

12. Proactive Risk Management in Generative AI

    Understand the importance of proactive risk management in ensuring the responsible use of generative AI.

13. How Generative AI Is Changing Creative Work

    Read the Harvard Business Review article on how generative AI is transforming creative work.

Additional Resources:

• Attention is All You Need

  Read the seminal paper introducing the attention mechanism, a crucial component in many language models.

• Transformer: A Novel Neural Network Architecture for Language Understanding

  Explore the foundational blog post introducing the Transformer architecture for language understanding.

• Transformer on Wikipedia

  Learn about the Transformer model, its introduction in 2017, and its impact on training models on larger datasets.

• What is Temperature in NLP?

  Understand the concept of temperature in Natural Language Processing (NLP) through this informative article.

• Model Garden

  Explore the Model Garden by Google Cloud, providing a collection of machine learning models and tools.

• Auto-generated Summaries in Google Docs

  Learn about the implementation of auto-generated summaries in Google Docs, enhancing document summarization.