4 min read

Large Language Models

A large language model, or LLM, is a deep learning algorithm that can recognize, summarize, translate, predict and generate text and other content based on knowledge gained from massive datasets. - Nvidia

Understanding how Large Language Models work will be one of the most significant advantages you can have as a decision-maker (creator, entrepreneur, operator, etc.)  over the next five years.

From TechCrunch:

“One reason these large language models remain so remarkable is that a single model can be used for tasks” including question answering, document summarization, text generation, sentence completion, translation and more, Bernard Koch, a computational social scientist at UCLA, told TechCrunch via email. “A second reason is because their performance continues to scale as you add more parameters to the model and add more data … The third reason that very large pre-trained language models are remarkable is that they appear to be able to make decent predictions when given just a handful of labeled examples.”

Startups including Cohere and AI21 Labs also offer models akin to GPT-3 through APIs. Other companies, particularly tech giants like Google, have chosen to keep the large language models they’ve developed in house and under wraps. For example, Google recently detailed — but declined to release — a 540 billion-parameter model called PaLM that the company claims achieves state-of-the-art performance across language tasks.

Large language models, open source or no, all have steep development costs in common. A 2020 study from AI21 Labs pegged the expenses for developing a text-generating model with only 1.5 billion parameters at as much as $1.6 million. Inference — actually running the trained model — is another drain. One source estimates the cost of running GPT-3 on a single AWS instance (p3dn.24xlarge) at a minimum of $87,000 per year.

As a decision-maker, building a solid understanding of large language models can have several benefits:

  1. Improved customer experience: Large language models can be used to develop chatbots, virtual assistants, and other conversational AI applications that can provide quick and effective customer service.
  2. Increased efficiency: Large language models can automate repetitive tasks, freeing up your time and resources to focus on higher-value activities.
  3. Improved decision making: Large language models can process vast amounts of data and provide insights that can inform business decisions. For example, a language model could analyze customer feedback and identify common pain points to inform product development.
  4. Competitive advantage: Large language models are becoming increasingly popular, and having a deep understanding of these technologies can give you an edge over competitors who are not using them.
  5. New business opportunities: As the use of large language models continues to grow, there will be new business opportunities that emerge. Having a strong understanding of the technology will position you to take advantage of these opportunities as they arise.

Overall, building a solid understanding of large language models can help decision-makers serve their customers better, improve their operations, and stay ahead of the curve in an ever-evolving Generative AI business landscape.

I've outlined the following self-study course outline and will be adding additional thoughts on this over the coming days:

Lesson 1: Introduction to Large Language Models (30 min)

  • Overview of AI and NLP
  • What are Large Language Models?
  • Brief history of NLP and its evolution
  • Importance of Large Language Models in AI
  • Explanation of GPT-3 and its significance
  • Overview of the course and expectations

Lesson 2: Understanding NLP and AI (30 min)

  • Explanation of NLP and its applications
  • Overview of AI and its different types
  • The role of NLP in AI
  • Explanation of Natural Language Processing Tasks
  • Differences between NLP and Computational Linguistics
  • Importance of NLP in AI

Lesson 3: Deep Learning in NLP (30 min)

  • Explanation of Deep Learning
  • Overview of Neural Networks
  • How Deep Learning is used in NLP
  • Explanation of Word Embeddings
  • Importance of Transfer Learning in NLP
  • Explanation of Transfer Learning in GPT-3

Lesson 4: Training and fine-tuning Large Language Models (30 min)

  • Explanation of Training and Fine-Tuning
  • Overview of Pre-training and Fine-Tuning
  • Explanation of Pre-training in GPT-3
  • Explanation of Fine-Tuning in GPT-3
  • The impact of fine-tuning on the performance of GPT-3
  • Explanation of Overfitting and Underfitting

Lesson 5: GPT-3 Architecture and its significance (30 min)

  • Explanation of GPT-3 Architecture
  • Overview of Transformer Networks
  • Explanation of Multi-Head Attention Mechanism
  • Explanation of Self-Attention Mechanism
  • Explanation of Position-wise Feed-Forward Networks
  • Explanation of GPT-3’s generative capabilities

Lesson 6: Applications of GPT-3 (30 min)

  • Overview of GPT-3 applications
  • Explanation of Text Generation
  • Explanation of Text Translation
  • Explanation of Text Summarization
  • Explanation of Text Classification
  • Explanation of Chatbots and Dialogue Generation

Lesson 7: GPT-3 Limitations and Challenges (30 min)

  • Explanation of GPT-3 Limitations
  • Overview of GPT-3’s ethical considerations
  • Explanation of GPT-3’s bias
  • Explanation of GPT-3’s vulnerability to adversarial attacks
  • Explanation of GPT-3’s resource requirements
  • Explanation of GPT-3’s limitations in understanding context

Lesson 8: Advancements in NLP and Large Language Models (30 min)

  • Overview of Advancements in NLP
  • Explanation of Graph-based NLP
  • Explanation of Neural Machine Translation
  • Explanation of Pre-training with Task-Specific Data
  • Explanation of Zero-shot Learning
  • Explanation of Adversarial Training

Lesson 9: Integrating GPT-3 in real-world applications (30 min)

  • Overview of Integrating GPT-3 in real-world applications
  • Explanation of GPT-3 APIs
  • Explanation of GPT-3 in Chatbots
  • Explanation of GPT-3 in Text Generation
  • Explanation of GPT-3 in Question Answering
  • Explanation of GPT-3 in Content Creation
  • Explanation of GPT-3 in Customer Service
  • Overview of GPT-3’s integration in different industries

Lesson 10: Conclusion and Future of Large Language Models (30 min)

  • Overview of the course
  • Explanation of the importance of Large Language Models in AI
  • Explanation of the impact of GPT-3 on the NLP industry
  • Discussion of the future of NLP and Large Language Models
  • Explanation of the ethical considerations and challenges that need to be addressed
  • Final thoughts and conclusion

Note: The time for each session can be adjusted based on the discussion and engagement of the audience.