1.Fine-tuning Techniques for LLMs
This lesson covers advanced methods for adapting pre-trained language models to specific tasks or domains. Topics include transfer
learning, few-shot learning, and parameter-efficient fine-tuning techniques like LoRA and prefix tuning. Students will learn how to
optimize model performance for targeted applications while minimizing computational resources.
2.Prompt Engineering and Optimization
This class focuses on the art and science of crafting effective prompts for LLMs. It covers prompt design patterns, chain-of-thought
prompting, and techniques for zero-shot and few-shot learning. Students will learn to optimize prompts for various tasks, understand
the impact of prompt phrasing on model outputs, and explore automated prompt optimization methods.
3.Ethical Considerations in LLM Development
This lesson delves into the ethical challenges posed by large language models. Topics include bias mitigation, fairness in AI, privacy
concerns, and the potential societal impacts of LLMs. Students will explore case studies, discuss regulatory frameworks, and learn
strategies for developing and deploying LLMs responsibly.
4.LLM Architecture Deep Dive
This class provides an in-depth look at the architectures of popular LLMs. It covers the evolution from RNNs to Transformers, and
examines specific architectures like GPT, BERT, and T5. Students will gain a detailed understanding of attention mechanisms, positional
encodings, and the scaling laws governing LLM performance.
5.Multimodal LLMs
This lesson explores the integration of multiple modalities in language models. It covers techniques for combining
text with images, audio, and video inputs. Topics include vision-language models, audio-text models, and recent advancements in
multimodal transformers. Students will learn about cross-modal attention and fusion techniques.
6.LLM Evaluation and Benchmarking
This class focuses on methods for assessing and comparing LLM performance. It covers popular benchmarks like GLUE and SuperGLUE, as
well as task-specific evaluation metrics. Students will learn about challenges in LLM evaluation, human evaluation techniques, and
strategies for creating robust evaluation frameworks for specific applications.
7.Efficient Training and Deployment of LLMs
This lesson addresses the challenges of working with resource-intensive language models. Topics include distributed
training, model parallelism, quantization, and pruning techniques. Students will explore methods for reducing model size while maintaining
performance, and learn about efficient inference techniques for deployment on various hardware platforms.
8.Domain-Specific LLM Applications
This class examines the application of LLMs to specific industries or domains. It covers techniques for domain adaptation, specialized
tokenization, and integration with domain-specific knowledge bases. Students will explore case studies in areas such as healthcare (medical diagnosis), finance (market analysis), and legal (contract analysis), learning how to tailor LLMs for specialized tasks.
9.Conversational AI and Dialogue Systems
This lesson focuses on building interactive AI systems using LLMs. It covers dialogue management, context handling, and techniques for
maintaining coherent long-term conversations. Students will learn about state tracking, response generation strategies, and methods
for incorporating external knowledge into conversational systems.
10. LLM Interpretability and Explainability
This class explores techniques for understanding and explaining the decision-making processes of LLMs. It covers attention visualization,
probing techniques, and methods for generating explanations for model outputs. Students will learn about the challenges of interpreting
black-box models and explore recent advancements in making LLMs more transparent and interpretable.
