Edge 457: Can we Distill Specific Knowledge in LLMs? An Intro to Attention-Based Distillation
Was this email forwarded to you? Sign up here Edge 457: Can we Distill Specific Knowledge in LLMs? An Intro to Attention-Based DistillationOne of the most interesting distillation techniques for foundation models.
In this issue:
💡 ML Concept of the Day: An Overview of Attention-Based DistillationAs 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... Subscribe to TheSequence to unlock the rest.Become a paying subscriber of TheSequence to get access to this post and other subscriber-only content. A subscription gets you:
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