Demystifying DPKD: How Preference Knowledge Distillation Boosts Small AI Models 🚀

Author(s): Aniket Sanyal

Originally published on Towards AI.

Introduction: Big Brains vs Small Brains in AI 🧠

Large Language Models (LLMs) like GPT-4 and other advanced chatbots have amazing capabilities, but they come with a catch: they are huge and computationally expensive. Imagine having a brilliant AI tutor that can answer anything, but it only runs on a supercomputer — not very practical for everyday apps or devices. What if we could shrink these AI brains into smaller models that are cheaper and faster, without losing too much of their intelligence? This is where knowledge distillation comes in.

In this article, the authors present Direct Preference Knowledge Distillation (DPKD), a new method that enhances traditional knowledge distillation by allowing a larger model to teach not just answers but also its preferences for good responses. The DPKD process is divided into two stages: first, aligning the student model with the teacher’s preferred answers, and second, fine-tuning the student to prioritize the teacher’s outputs. This approach shows substantial improvements over standard training methods, leading to student models that perform closer to their larger counterparts across various tasks and complexities, paving the way for more efficient AI deployments and offering insights into model training methodologies.

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Published via Towards AI