Aligning large language models to human values with math and algorithms
Aligning Large Language Models with Human Feedback: Mathematical Foundations and Algorithm Design
Machine LearningComputation and Language
Summary
Large language models sometimes give answers that don’t match what people really want or expect. This paper explains the math and step-by-step methods that help adjust those models to align better with human opinions and values. It covers common techniques like training on examples, using feedback from people to guide learning, and optimizing directly for what people prefer. The authors also show how these ideas connect to other theories about learning from choices. Their goal is to provide a clear guide for those building safer and more useful language models.
What this means in practice
- •For machine learning engineers: Develop more accurate and efficient training pipelines that incorporate human feedback for better language model outputs.
- •For product managers in ai companies: Design user-facing features that rely on language models better tuned to user preferences, improving satisfaction and safety.$Commercial implications: Enables creation of AI products that can be marketed as personalized and ethically aligned, increasing user trust and adoption.
A survey. It maps existing work.
Authors
Siliang Zeng, Luca Viano, Chenliang Li, Jiaxiang Li, Volkan Cevher, Markus Wulfmeier, Stefano Ermon, Alfredo Garcia, Mingyi Hong
Abstract
This article provides an introduction to the mathematical foundations and algorithmic frameworks used to align Large Language Models (LLMs) with human intentions, preferences, and values. We discuss standard alignment techniques, such as fine-tuning (SFT), reinforcement learning with human feedback (RLHF), and direct preference optimization (DPO). We also explore the theoretical underpinnings of learning from human preferences, drawing connections to inverse reinforcement learning (IRL) and discrete choice models. We present state-of-the-art algorithms in a tutorial style, discuss their advantages and limitations, and offer insights into practical implementation. Our exposition is intended to serve as a comprehensive resource for researchers and practitioners, providing both a foundational understanding of alignment methodologies and a framework for developing more robust and scalable alignment techniques.