Advanced LLMs for NLP Tasks

• 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

Large language models (LLMs) are AI models that can process and generate large amounts of natural language data, such as text, speech, and audio. LLMs can learn the patterns and structure of their input training data and then generate new data that has similar characteristics. LLMs can also perform various natural language processing (NLP) tasks, such as natural language understanding (NLU), natural language inference (NLI), knowledge graph construction and completion, commonsense reasoning, dialogue generation and management, and multimodal generation and understanding.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data scientists, AI developers, and AI enthusiasts who wish to use LLMs to perform various NLP tasks and create novel and diverse content for different purposes.

By the end of this training, participants will be able to:

• Establish a development environment with LLMs and essential tools.
• Expertly perform NLU and NLI tasks with LLMs.
• Extract, infer, and utilize knowledge graphs effectively.
• Generate and manage dialogues using LLMs for conversational applications.
• Evaluate content quality and diversity generated by LLMs and generative AI.
• Apply ethical principles, ensuring fairness and responsible use of LLMs.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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