Advanced LLMs for NLP Tasks Training Course
Large language models (LLMs) constitute a class of artificial intelligence systems capable of processing and generating extensive volumes of natural language data, including text, speech, and audio. These models analyze patterns and structural elements within training datasets to produce new content that mirrors established characteristics. LLMs facilitate a broad range of natural language processing (NLP) functions, including natural language understanding (NLU), natural language inference (NLI), the construction and completion of knowledge graphs, commonsense reasoning, the generation and management of dialogue, and multimodal generation and comprehension.
This instructor-led, live training program, delivered either online or on-site, is designed for intermediate-level data scientists, AI developers, and technology professionals seeking to leverage LLMs for executing complex NLP tasks and producing innovative, diverse content for organizational purposes.
Upon completion of this training, participants will be equipped to:
- Configure a development environment incorporating LLMs and essential toolkits.
- Execute NLU and NLI tasks with proficiency using LLMs.
- Efficiently extract, infer, and deploy knowledge graphs.
- Generate and manage dialogue structures using LLMs for conversational applications.
- Evaluate the quality and diversity of content produced by LLMs and generative AI systems.
- Implement ethical principles to ensure fairness and responsible utilization of LLMs.
Course Delivery Format
- Interactive lectures facilitated by structured discussion.
- Comprehensive exercises and practical application tasks.
- Hands-on implementation within a live laboratory setting.
Customization Provisions
- To initiate a customized training program for this course, please contact our office to arrange specific requirements.
Course Outline
Introduction to Large Language Models and Generative AI
- Examining architectural techniques and model designs
- Reviewing operational applications and sector-specific use cases
- Identifying technical constraints and operational challenges
Applying Large Language Models for Natural Language Understanding
- Sentiment analysis implementation
- Named entity recognition processes
- Relation extraction methodologies
- Semantic parsing frameworks
Applying Large Language Models for Natural Language Inference
- Entailment detection procedures
- Contradiction identification techniques
- Paraphrase recognition methods
Utilizing Large Language Models for Knowledge Graph Construction
- Extracting factual data and relational structures from text
- Inferring absent or novel factual elements
- Deploying knowledge graphs for downstream operational tasks
Employing Large Language Models for Commonsense Reasoning
Using Large Language Models for Dialogue Generation
- Creating interactions with conversational agents, chatbots, and virtual assistants
- Administering dialogue management protocols
- Utilizing dialogue datasets and performance metrics
Deploying Large Language Models for Multimodal Generation
- Synthesizing images from textual input
- Generating textual descriptions from image input
- Producing video content from text or image sources
- Generating audio output from textual prompts
- Converting audio input into text
- Constructing 3D models from text or image data
Integrating Large Language Models for Meta-Learning
- Adapting large language models to new domains, specific tasks, or languages
- Learning from limited-shot or zero-shot examples
- Applying meta-learning and transfer learning frameworks and datasets
Implementing Large Language Models for Adversarial Learning
- Safeguarding large language models against malicious intrusions
- Detecting and mitigating biases and systematic errors
- Employing adversarial learning strategies and robustness methodologies
Evaluating Large Language Models and Generative AI
- Assessing the quality and diversity of generated content
- Applying metrics such as inception score, Fréchet inception distance, and BLEU score
- Utilizing human-centric evaluation methods, including crowdsourcing and surveys
- Employing adversarial evaluation techniques, such as Turing tests and discriminators
Applying Ethical Principles to Large Language Models and Generative AI
- Ensuring fairness and institutional accountability
- Preventing misuse and improper application
- Protecting the rights and privacy of content creators and consumers
- Promoting collaborative innovation between humans and artificial intelligence
Summary and Future Directives
Requirements
- Proficiency in fundamental AI concepts and terminology
- Practical experience with Python programming and data analytics
- Familiarity with deep learning frameworks, such as TensorFlow or PyTorch
- Working knowledge of LLM fundamentals and their applicability
Target Audience
- Data Scientists
- AI Developers
- AI Enthusiasts
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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