Advanced LLMs for NLP Tasks Training Course
Large language models (LLMs) are AI models capable of processing and generating vast quantities of natural language data, including text, speech, and audio. These models learn the patterns and structures inherent in their training data to produce new content with similar attributes. Additionally, LLMs facilitate 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 understanding.
This instructor-led, live training (available online or onsite) is designed for intermediate-level data scientists, AI developers, and AI enthusiasts looking to leverage LLMs for various NLP tasks and to create novel, diverse content for diverse objectives.
Upon completion of this training, participants will be able to:
- Set up a development environment equipped with LLMs and necessary tools.
- Proficiently execute NLU and NLI tasks using LLMs.
- Effectively extract, infer, and utilize knowledge graphs.
- Generate and manage dialogues using LLMs for conversational applications.
- Evaluate the quality and diversity of content generated by LLMs and generative AI.
- Apply ethical principles to ensure fairness and responsible use of LLMs.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practice opportunities.
- Hands-on implementation in a live-lab 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 against 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.
- Utilizing metrics such as inception score, Fréchet inception distance, and BLEU score.
- Employing human evaluation methods like crowdsourcing and surveys.
- Utilizing 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 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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