Advanced LLMs for NLP Tasks Training Course
Large language models (LLMs) are artificial intelligence systems designed to process and generate extensive amounts of natural language data, including text, speech, and audio. These models can learn the patterns and structure from their input training data and subsequently produce new content with similar characteristics. LLMs are capable of performing 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 leverage LLMs for performing various NLP tasks and creating innovative and diverse content for government applications.
By the end of this training, participants will be able to:
- Set up a development environment with LLMs and essential tools for government use.
- Expertly perform NLU and NLI tasks using LLMs.
- Extract, infer, and utilize knowledge graphs effectively in public sector workflows.
- Generate and manage dialogues using LLMs for conversational applications within the government context.
- Evaluate the quality and diversity of content generated by LLMs and generative AI to ensure alignment with governmental standards.
- Apply ethical principles, ensuring fairness and responsible use of LLMs in public sector operations.
Format of the Course
- Interactive lecture and discussion tailored for government professionals.
- Lots of exercises and practice relevant to governmental tasks.
- Hands-on implementation in a live-lab environment designed for government applications.
Course Customization Options
- To request a customized training for government, please contact us to arrange.
Course Outline
Introduction to LLMs and Generative AI for Government
- Exploring techniques and models
- Discussing applications and use cases
- Identifying challenges and limitations
Using LLMs for NLU Tasks in Public Sector Workflows
- Sentiment analysis
- Named entity recognition
- Relation extraction
- Semantic parsing
Utilizing LLMs for NLI Tasks for Government
- Entailment detection
- Contradiction detection
- Paraphrase detection
Leveraging LLMs for Knowledge Graphs in Governance
- Extracting facts and relations from text
- Inferring missing or new facts
- Using knowledge graphs for downstream tasks
Employing LLMs for Commonsense Reasoning for Government
- Generating plausible explanations, hypotheses, and scenarios
- Using commonsense knowledge bases and datasets
- Evaluating commonsense reasoning
Implementing LLMs for Dialogue Generation in Public Sector Communications
- Generating dialogues with conversational agents, chatbots, and virtual assistants
- Managing dialogues
- Using dialogue datasets and metrics
Utilizing LLMs for Multimodal Generation in Government Services
- 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
Leveraging LLMs for Meta-Learning in Government Applications
- 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
Defending LLMs for Government Against Adversarial Threats
- 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 for Government
- 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 in Government
- 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 for Government Implementation
Requirements
- An understanding of fundamental AI concepts and terminology for government use.
- Experience with Python programming and data analysis in public sector contexts.
- Familiarity with deep learning frameworks such as TensorFlow or PyTorch, tailored for government applications.
- An understanding of the basics of Large Language Models (LLMs) and their potential applications for government operations.
Audience
- Data scientists working in governmental agencies.
- AI developers focused on government projects.
- AI enthusiasts interested in public sector innovation.
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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