Advanced LLMs for NLP Tasks Training Course
Large language models (LLMs) are AI models capable of processing and generating vast amounts of natural language data, including text, speech, and audio. These models learn the patterns and structures within their training data to produce new content with similar characteristics. Additionally, LLMs can handle 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 comprehension.
This instructor-led live training, available online or onsite, targets intermediate-level data scientists, AI developers, and AI enthusiasts looking to leverage LLMs for diverse NLP tasks and to create innovative, varied content for various applications.
Upon completing 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.
- Create and manage dialogues with LLMs for conversational applications.
- Assess the quality and diversity of content generated by LLMs and generative AI.
- Implement ethical principles to ensure fairness and responsible LLM usage.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical sessions.
- Hands-on implementation in a live-lab setting.
Course Customization Options
- For customized training requests, please contact us to arrange.
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
- Applying knowledge graphs to downstream tasks
Using LLMs for Commonsense Reasoning
- Generating plausible explanations, hypotheses, and scenarios
- Leveraging commonsense knowledge bases and datasets
- Evaluating commonsense reasoning capabilities
Using LLMs for Dialogue Generation
- Creating dialogues with conversational agents, chatbots, and virtual assistants
- Managing dialogue flows
- Utilizing dialogue datasets and evaluation 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
- Utilizing meta-learning and transfer learning datasets and frameworks
Using LLMs for Adversarial Learning
- Defending LLMs against malicious attacks
- Detecting and mitigating biases and errors in LLMs
- Applying 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
- Employing human evaluation methods such as crowdsourcing and surveys
- Utilizing adversarial evaluation methods like Turing tests and discriminators
Applying Ethical Principles for LLMs and Generative AI
- Ensuring fairness and accountability
- Preventing misuse and abuse
- Respecting the rights and privacy of content creators and consumers
- Promoting creativity and collaboration between humans 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
- Understanding of LLM basics and their applications
Audience
- Data scientists
- AI developers
- AI enthusiasts
Open Training Courses require 5+ participants.
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