TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models
Makoto Shing, Kou Misaki, Han Bao, Sho Yokoi, Takuya Akiba·January 28, 2025
Summary
TAID, introduced at ICLR 2025, offers a dynamic knowledge distillation method for efficient model transfer in language models. It addresses capacity gaps, mode averaging, and collapse by interpolating between student and teacher distributions. This method enables the creation of compact, high-performing models like TAID-LLM-1.5B and TAID-VLM-2B for language and vision-language tasks, advancing AI accessibility. TAID outperforms competitors on ImageNet and shows superior performance in complex tasks, with new state-of-the-art models achieving high performance across various domains.
Introduction
Background
Overview of knowledge distillation techniques in language models
Challenges in model transfer, including capacity gaps, mode averaging, and collapse
Objective
To introduce and explain the TAID method, a dynamic knowledge distillation approach for efficient model transfer in language models
Method
Data Collection
Description of the datasets used for training and testing TAID models
Data Preprocessing
Techniques applied to prepare the data for TAID's dynamic knowledge distillation process
Interpolation between Student and Teacher Distributions
Detailed explanation of how TAID interpolates between student and teacher distributions to address capacity gaps, mode averaging, and collapse
TAID Models
TAID-LLM-1.5B
Characteristics and performance of the TAID-LLM-1.5B model for language tasks
TAID-VLM-2B
Features and achievements of the TAID-VLM-2B model for vision-language tasks
Advancements in AI Accessibility
Compact, High-Performing Models
Discussion on how TAID enables the creation of compact models with high performance
State-of-the-Art Performance
Overview of TAID's superior performance on ImageNet and its achievements in complex tasks
Conclusion
Summary of TAID's Contributions
Recap of TAID's impact on knowledge distillation and model transfer in language models
Future Directions
Potential areas for further research and development with TAID
Basic info
papers
computation and language
machine learning
artificial intelligence
Advanced features
Insights
What is TAID and where was it introduced?
What are the main issues that TAID addresses in the context of model transfer?
How does TAID compare to its competitors in terms of performance on ImageNet and complex tasks?
What are some of the high-performing models created using TAID and what tasks are they designed for?