Aligning Open-source LLMs Using Reinforcement Learning from Feedback

Abstract: 

Unlock the full potential of open-source Large Language Models (LLMs) in our alignment workshop focused on using supervised fine-tuning (SFT) and reinforcement learning (RL) to optimize LLM performance. With LLMs like ChatGPT and Llama 2 revolutionizing the field of AI, mastering the art of fine-tuning these models for optimal human interaction has become crucial.

Throughout the session, we will focus on the core concepts of LLM fine-tuning, with a particular emphasis on reinforcement learning mechanisms. Engaging in hands-on exercises, attendees will gain practical experience in data preprocessing, quality assessment, and implementing reinforcement learning techniques for manual alignment. This skill set is especially valuable for achieving instruction-following capabilities and much more.

The workshop will provide a comprehensive understanding of the challenges and intricacies involved in aligning LLMs. By learning to navigate through data preprocessing and quality assessment, participants will gain insights into identifying the most relevant data for fine-tuning LLMs effectively. Moreover, the practical application of reinforcement learning techniques will empower attendees to tailor LLMs for specific tasks, ensuring enhanced performance and precision in real-world applications.

By the workshop's conclusion, attendees will be well-equipped to harness the power of open-source LLMs effectively, tailoring their models to meet the specific demands of their industries or domains. Don't miss out on this opportunity to learn how to create your very own instruction-aligned LLM and enhance your AI applications like never before!

Session Outline:

Lesson 1: Understanding Reinforcement Learning from Human Feedback (RLHF) and AI Feedback (RLAIF)
Attendees will be introduced to the mechanisms of Reinforcement Learning from Human Feedback (RLHF) and AI Feedback (RLAIF). Throughout this lesson, participants will grasp the core concepts and strategies involved in collecting and leveraging human feedback to align language models. At the end of this lesson, attendees will have a comprehensive understanding of how RLHF and RLAIF can be applied to optimize language models effectively.

Lesson 2: Aligning FLAN-T5 for Customized Summaries
Participants will be guided through the process of aligning FLAN-T5, a language model, to generate more customized summaries. We will see techniques for aligning the model with specific data or user instructions, enabling FLAN-T5 to produce summaries that cater to individual requirements. By the end of this lesson, participants will be equipped with the skills to create highly personalized summaries with FLAN-T5.

Lesson 3: Fine-Tuning Open Source Llama 2 for Instruction Following
In this lesson, participants will focus on aligning the open-source Llama 2 to follow instructions. We will see how to optimize LLama 2's behavior by manually aligning it with high-quality data and see the caveats on how pre-training data can affect an aligned model's behavior. By the end of this lesson, participants will have the practical knowledge to effectively fine-tune open source LLMs using supervised fine-tuning and reinforcement learning to improve their ability to understand and respond to instructions.

Learning Objectives:

Understanding RLHF and RLAIF: Attendees will gain a clear understanding of the mechanisms behind Reinforcement Learning from Human Feedback (RLHF) and AI Feedback (RLAIF). They will see how these approaches can be leveraged to align language models.

Aligning Open-Source LLMs for Customized Use: Participants will learn practical techniques for aligning models with specific data or user instructions to produce tailored generations.

Applying RLHF and RLAIF Techniques: After the session, participants will be equipped to apply RLHF and RLAIF techniques to various language models. They will explore real-world use cases across different industries, discovering how to leverage these approaches for human feedback and iterative fine-tuning, leading to more specialized and efficient language models.

Open Source Tools:
During the presentation, we will primarily use the following open-source tools:

Hugging Face Transformers Library: This library offers a range of pre-trained language models, including FLAN-T5 and LLama 2, and allows fine-tuning and alignment for various natural language processing tasks.

TRL Library: This library can be used for Reinforcement Learning and can be integrated with code using the Transformers library.

Colab: We will conduct hands-on exercises and demonstrations using Colab, providing an interactive environment for attendees to follow along with the practical aspects of the session.

GitHub: All code, examples, and resources used in the session will be made available on GitHub, allowing participants to access and refer back to the materials for further exploration and self-study after the workshop.

By the end of the session, attendees will be equipped with valuable knowledge and practical skills to align and fine-tune language models effectively using RLHF and RLAIF approaches, unlocking the full potential of these models for various language processing tasks and applications.

Background Knowledge:

- Loading and creating generations with LLMs using the Transformers library
- Fine-tuning LLMs using labeled data in a supervised manner

Bio: 

Sinan Ozdemir is a mathematician, data scientist, NLP expert, lecturer, and accomplished author. He is currently applying my extensive knowledge and experience in AI and Large Language Models (LLMs) as the founder and CTO of LoopGenius, transforming the way entrepreneurs and startups market their products and services.

Simultaneously, he is providing advisory services in AI and LLMs to Tola Capital, an innovative investment firm. He has also worked as an AI author for Addison Wesley and Pearson, crafting comprehensive resources that help professionals navigate the complex field of AI and LLMs.

Previously, he served as the Director of Data Science at Directly, where my work significantly influenced their strategic direction. As an official member of the Forbes Technology Council from 2017 to 2021, he shared his insights on AI, machine learning, NLP, and emerging technologies-related business processes.

He holds a B.A. and an M.A. in Pure Mathematics (Algebraic Geometry) from The Johns Hopkins University, and he is an alumnus of the Y Combinator program. Sinan actively contribute to society through various volunteering activities.

Sinan's skill set is strongly endorsed by professionals from various sectors and includes data analysis, Python, statistics, AI, NLP, theoretical mathematics, data science, function analysis, data mining, algorithm development, machine learning, game-theoretic modeling, and various programming languages.

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