15. Modern Topics – LLMs & Foundation Models
Large Language Models (LLMs) and Foundation Models have transformed machine learning, especially in natural language processing, vision, and code generation. This chapter explores their architectures, training methods, applications, and ethical implications. Emphasizing the role of transformer architecture, it highlights both the potential and the challenges these models introduce in AI applications.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Foundation models serve as large-scale pre-trained models for various downstream tasks.
- Transformer architecture is key to the success and scalability of LLMs.
- Ethical concerns include bias, hallucination, and the environmental impact of training large models.
Key Concepts
- -- Foundation Models
- Large, pre-trained models that can be adapted to a variety of tasks, enhancing scalability and reuse.
- -- Large Language Models (LLMs)
- Foundation models primarily trained on textual data to understand and generate human language.
- -- Transformer Architecture
- A model architecture based on self-attention mechanisms, enabling efficient processing of sequential data.
- -- Scaling Laws
- The relationship showing that larger models generally perform better when trained properly on extensive datasets.
- -- Bias and Fairness
- Concerns that models may reflect societal biases from their training data, potentially leading to harmful outcomes.
Additional Learning Materials
Supplementary resources to enhance your learning experience.