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Course Outline
Introduction to LLMs and Generative AI
- Exploring techniques and models
- Discussing applications and use cases
- Identifying challenges and limitations
Using LLMs for NLU Tasks
- Sentiment analysis
- Named entity recognition
- Relation extraction
- Semantic parsing
Using LLMs for NLI Tasks
- Entailment detection
- Contradiction detection
- Paraphrase detection
Using LLMs for Knowledge Graphs
- Extracting facts and relations from text
- Inferring missing or new facts
- Using knowledge graphs for downstream tasks
Using LLMs for Commonsense Reasoning
- Generating plausible explanations, hypotheses, and scenarios
- Using commonsense knowledge bases and datasets
- Evaluating commonsense reasoning
Using LLMs for Dialogue Generation
- Generating dialogues with conversational agents, chatbots, and virtual assistants
- Managing dialogues
- Using dialogue datasets and metrics
Using LLMs for Multimodal Generation
- Generating images from text
- Generating text from images
- Generating videos from text or images
- Generating audio from text
- Generating text from audio
- Generating 3D models from text or images
Using LLMs for Meta-Learning
- Adapting LLMs to new domains, tasks, or languages
- Learning from few-shot or zero-shot examples
- Using meta-learning and transfer learning datasets and frameworks
Using LLMs for Adversarial Learning
- Defending LLMs from malicious attacks
- Detecting and mitigating biases and errors in LLMs
- Using adversarial learning and robustness datasets and methods
Evaluating LLMs and Generative AI
- Assessing content quality and diversity
- Using metrics like inception score, Fréchet inception distance, and BLEU score
- Using human evaluation methods like crowdsourcing and surveys
- Using adversarial evaluation methods like Turing tests and discriminators
Applying Ethical Principles for LLMs and Generative AI
- Ensuring fairness and accountability
- Avoiding misuse and abuse
- Respecting the rights and privacy of content creators and consumers
- Fostering creativity and collaboration of human and AI
Summary and Next Steps
Requirements
- An understanding of basic AI concepts and terminology
- Experience with Python programming and data analysis
- Familiarity with deep learning frameworks such as TensorFlow or PyTorch
- An understanding of the basics of LLMs and their applications
Audience
- Data scientists
- AI developers
- AI enthusiasts
21 Hours
Testimonials (1)
Exercises and exchanges during questions/answers
Antoine - Physiobotic
Course - Scaling Data Pipelines with Spark NLP
Machine Translated
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