Chain-of-Thought (CoT) Distillation from An Information Bottleneck Perspective

In this work, we delve into the Chain-of-Thought (CoT) Distillation problem, presenting it through the lens of information theory. Our approach re-examines CoT distillation and places it within an information bottleneck framework. By doing so, we offer a novel theoretical perspective that sheds light on the intricacies of distillation processes in natural language processing.

Distillation is a technique used to transfer knowledge from a large model to a smaller one, but it often requires large amounts of data and computational resources.

**This work is collaboration with Dr.Xin Chen (Applied ML Lab, Intel Corp.), Hanxian Huang (final year PhD student at UCSD). Thank their contribution to this project! **

Key Problems Addressed

Efficiency of CoT Distillation: Traditional distillation methods can be inefficient, particularly when it comes to capturing and transferring the intricate reasoning steps involved in CoT processes. Our paper seeks to improve the efficiency of this distillation process.

Data Requirements: High data requirements for effective distillation pose a barrier to training smaller models, especially in resource-constrained environments. Our research aims to reduce the amount of data needed for successful distillation.

Our Contributions

Information Theory Framework: We re-examine the CoT distillation problem through the lens of information theory. By applying an information bottleneck perspective, we provide a novel theoretical framework that enhances understanding and efficiency.

Novel Loss Function: We propose a new loss function designed to maximize mutual information. This function improves the efficiency of the distillation process, allowing smaller models to be trained effectively with less data.

Multi-task Setting: Our approach is evaluated in a multi-task setting, demonstrating its versatility and potential impact across various NLP tasks.

What’s Next?

For those interested in a deeper dive, the preprint of our paper is already available on arXiv.

We welcome feedback and discussions from fellow researchers and practitioners.

Thank you for your continued support and interest in our work. Stay tuned for more updates and insights as we prepare for the conference!