Advanced LLMs for NLP Tasks Training Course
Large Language Models (LLMs) are artificial intelligence systems capable of processing and generating vast quantities of natural language data, including text, speech, and audio. By learning the patterns and structures within their training data, LLMs can produce new content that shares similar characteristics. Additionally, these models can execute a wide range of 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 comprehension.
This instructor-led, live training (available online or onsite) is designed for intermediate-level data scientists, AI developers, and AI enthusiasts who aim to leverage LLMs to perform diverse NLP tasks and create novel, varied content for specific objectives.
Upon completion of this training, participants will be able to:
- Set up a development environment equipped with LLMs and essential tools.
- Proficiently execute NLU and NLI tasks using LLMs.
- Effectively extract, infer, and apply knowledge graphs.
- Generate and manage dialogues using LLMs for conversational applications.
- Assess the quality and diversity of content produced by LLMs and generative AI.
- Implement ethical principles to ensure fairness and responsible usage of LLMs.
Format of the Course also allows for the evaluation of participants.
- Interactive lectures and discussions.
- Extensive exercises and practice sessions.
- Hands-on implementation within a live laboratory environment.
Course Customization Options
- To request customized training for this course, please contact us to make arrangements.
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
- A solid understanding of fundamental AI concepts and terminology.
- Experience with Python programming and data analysis.
- Familiarity with deep learning frameworks such as TensorFlow or PyTorch.
- An understanding of LLM fundamentals and their practical applications.
Audience
- Data scientists
- AI developers
- AI enthusiasts
Open Training Courses require 5+ participants.
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