Edge 457: Can we Distill Specific Knowledge in LLMs? An Intro to Attention-Based Distillation
One of the most interesting distillation techniques for foundation models.
In this issue:
An overview of attention-based distillation(ABD).
A review of one of the most relevant ABD papers.
An introduction to Microsoft’s famous OmniParser vision based GUI agent.
💡 ML Concept of the Day: An Overview of Attention-Based Distillation
As part of our series about knowledge distillation, we have mostly focused on methods that match features from a teacher model to a student model. But what if we can distill more specific forms of knowledge? This is the core focus of attention-based distillation(ABD) techniques.
ABD is an advanced knowledge transfer technique that leverages the power of attention mechanisms to distill knowledge from a large teacher model to a smaller student model. Unlike traditional distillation methods that focus solely on matching logits or intermediate features, ABD aims to transfer the teacher's attention patterns, capturing the reasoning process behind the model's decisions. At its core, ABD forces the student network to mimic the attention maps generated by the teacher network. This comprehensive knowledge transfer often results in student models that achieve higher performance with fewer parameters compared to other distillation techniques.