Advanced LLMs for NLP Tasks Training Course

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