TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models
Makoto Shing, Kou Misaki, Han Bao, Sho Yokoi, Takuya Akiba
2025-01-29
Summary
This paper talks about a new method called TAID (Temporally Adaptive Interpolated Distillation) that helps make big, smart AI language models smaller and more efficient without losing too much of their intelligence.
What's the problem?
Big AI language models are really smart, but they're also huge and need a lot of computer power to run. This makes it hard to use them on regular computers or phones. When we try to make them smaller using a technique called knowledge distillation, we run into problems because the big 'teacher' model is so different from the smaller 'student' model we're trying to create.
What's the solution?
The researchers came up with TAID, which is like a smart tutor for AI. TAID helps the small 'student' model learn from the big 'teacher' model bit by bit. It does this by creating a middle ground that slowly changes over time, helping the student model understand the teacher's knowledge without getting overwhelmed. They tested TAID on different types and sizes of AI models and found that it works really well. They even used TAID to create two new, smaller AI models that are really good at language tasks and tasks that involve both language and images.
Why it matters?
This matters because it could make powerful AI more accessible to everyone. If we can shrink these smart AI models without making them much less intelligent, we could use them on regular computers and phones. This could lead to better AI assistants, more advanced apps, and new tools that can understand and process language and images in ways that weren't possible before. It's like finding a way to put a genius brain into a small, portable device that anyone can use.
Abstract
Causal language models have demonstrated remarkable capabilities, but their size poses significant challenges for deployment in resource-constrained environments. Knowledge distillation, a widely-used technique for transferring knowledge from a large teacher model to a small student model, presents a promising approach for model compression. A significant remaining issue lies in the major differences between teacher and student models, namely the substantial capacity gap, mode averaging, and mode collapse, which pose barriers during distillation. To address these issues, we introduce Temporally Adaptive Interpolated Distillation (TAID), a novel knowledge distillation approach that dynamically interpolates student and teacher distributions through an adaptive intermediate distribution, gradually shifting from the student's initial distribution towards the teacher's distribution. We provide a theoretical analysis demonstrating TAID's ability to prevent mode collapse and empirically show its effectiveness in addressing the capacity gap while balancing mode averaging and mode collapse. Our comprehensive experiments demonstrate TAID's superior performance across various model sizes and architectures in both instruction tuning and pre-training scenarios. Furthermore, we showcase TAID's practical impact by developing two state-of-the-art compact foundation models: TAID-LLM-1.5B for language tasks and TAID-VLM-2B for vision-language tasks. These results demonstrate TAID's effectiveness in creating high-performing and efficient models, advancing the development of more accessible AI technologies.