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Large Language Models (LLMs)

Aligning Large Language Models

Published by Pearson

Intermediate content levelIntermediate

Techniques to align models with your goals, ethics, and real-world applications

  • In-depth exploration of various alignment techniques with hands-on case studies, such as Constitutional AI
  • Comprehensive coverage of evaluating alignment, offering specific tools and metrics for continuous assessment and adaptation of LLM alignment strategies
  • A focus on ethical considerations and future directions, ensuring participants not only understand the current landscape but are also prepared for emerging trends and challenges in LLM alignment

This class is an intensive exploration into the alignment of Large Language Models (LLMs), a vital topic in modern AI development. Through a combination of theoretical insights and hands-on practice, participants will be exposed to various alignment techniques, including a focus on Constitutional AI, constructing reward mechanisms from human feedback, and instructional alignment. The course will also provide detailed guidance on evaluating alignment, with specific tools and metrics to ensure that models align with desired goals, ethical standards, and real-world applications.

The importance of alignment in LLMs cannot be overstated, as it ensures that models act in accordance with human values and guidelines, preventing potential biases and misuses. This course stands out by providing not only a comprehensive understanding of existing alignment methods but also a forward-looking perspective on ethical considerations and future trends. Whether for researchers, engineers, or practitioners, mastering these aspects of alignment is essential for responsible and effective utilization of LLMs in various domains.

What you’ll learn and how you can apply it

By the end of the live online course, you’ll understand:

  • The fundamental principles and various techniques for aligning Large Language Models with ethical guidelines and specific goals.
  • The complexities and challenges associated with LLM alignment, and the strategies to overcome them.
  • The step-by-step process of instructional alignment, including supervised fine-tuning, reward model training, and reinforcement learning.
  • The ethical considerations and future research directions in alignment, preparing you for the evolving landscape of LLM development.

And you’ll be able to:

  • Implement different alignment techniques, including Constitutional AI and instructional alignment, in real-world scenarios.
  • Evaluate and continuously assess alignment using specific tools and metrics tailored for LLMs.
  • Design and adapt alignment strategies for specific applications, ensuring that models are responsibly aligned with user needs and ethical standards.
  • Engage with the ongoing research and innovation in alignment, keeping your skills and understanding at the cutting edge of the field.

This live event is for you because...

  • You are a data scientist, AI researcher, machine learning engineer, or AI ethics professional at an intermediate or advanced level, seeking a deep, practical understanding of alignment in Large Language Models.
  • You're looking to implement alignment in current projects or explore the latest trends and challenges in a course that offers the hands-on experience and expert guidance needed to master LLM alignment.

Prerequisites

  • Basic understanding of machine learning concepts and algorithms.
  • Familiarity with Large Language Models such as GPT-3 or T5.
  • Experience with programming languages like Python, particularly in the context of data manipulation and model training.
  • Knowledge of reinforcement learning principles would be beneficial but is not mandatory.

Course Set-up

  • Attendees will need access to Python and an environment to run Jupyter notebooks.
  • Access to the internet for downloading and accessing course materials.
  • A GitHub repository containing all the necessary code and resources: https://github.com/sinanuozdemir/oreilly-llm-alignment

Recommended Preparation

Recommended Follow-up

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Segment 1: Introduction to Alignment in LLMs (20 minutes)

  • Definition, importance, and fundamental concepts of alignment.
  • Challenges in aligning LLMs with human values and expectations.
  • A look at real-world examples of alignment and how alignment leads to more capable AI systems.

Segment 2: Techniques and Best Practices for Alignment (30 minutes)

  • A look at the general components of alignment: supervised fine-tuning, reward modeling, and reinforcement learning and how they impact interpretability of AI systems.
  • Case Study: Constitutional AI - RLAIF by writing and implementing a set of ethical guidelines to align an LLM.

Q&A (10 minutes)

Break (10 minutes)

Segment 3: Instructional Alignment Case Study (50 minutes)

  • Walking through a real example of supervised fine-tuning, reward model training, and reinforcement learning.
  • Case Study + Workshop: Practical application of instructional alignment.

Q&A (10 minutes)

Break (10 minutes)

Segment 4: Evaluating Alignment (35 minutes)

  • Introduction to tools and metrics for evaluating alignment.
  • Assessing the effectiveness of alignment strategies through predefined criteria.
  • Strategies for continuous evaluation and adaptation.
  • Workshop: Evaluate a pre-aligned LLM using given metrics and tools.

Q&A (10 minutes)

Break (10 minutes)

Segment 5: Ethical Considerations and Future Direction (35 minutes)

  • Ethical concerns in alignment, including biases and fairness.
  • Future research directions and emerging trends in alignment with LLMs.

Q&A + Course wrap-up and next steps (10 minutes)

Your Instructor

  • Sinan Ozdemir

    Sinan Ozdemir is founder and CTO of LoopGenius, where he uses state-of-the-art AI to help people create and run their businesses. He has lectured in data science at Johns Hopkins University and authored multiple books, videos and numerous online courses on data science, machine learning, and generative AI. He also founded the recently acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. Sinan most recently published Quick Guide to Large Language Models, and launched a podcast audio series, AI Unveiled. Ozdemir holds a master’s degree in pure mathematics from Johns Hopkins University.

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