SRA: Span Representation Alignment for Large Language Model Distillation

2023-03-30 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, long

Summary

SRA (Span Representation Alignment for Large Language Model Distillation) is a novel framework designed to facilitate Cross-Tokenizer Knowledge Distillation (CTKD) between large teacher models and smaller student models, even when they use different tokenizers. Unlike existing CTKD methods that focus on brittle token-level alignment, SRA shifts the fundamental unit of alignment to robust, tokenizer-agnostic spans. It models each span as a cluster of particles, representing its state by its Center of Mass (CoM), an attention-weighted average that captures rich semantic information. SRA leverages attention-derived weighting to prioritize salient spans and employs a geometric regularizer to preserve the structural integrity of the representation space. The framework also introduces aligned span logit distillation to enhance knowledge transfer. This physically-grounded approach consistently and significantly outperforms state-of-the-art CTKD baselines in cross-architecture distillation experiments.

Key takeaway

For AI Engineers and Research Scientists working on model compression or cross-architecture deployment, SRA offers a robust method to distill knowledge across models with different tokenizers. By focusing on span-level alignment and leveraging attention-weighted representations, you can achieve more effective knowledge transfer than traditional token-level approaches. Consider integrating span-based distillation and geometric regularization into your next model compression pipeline to improve student model performance and stability.

Key insights

Aligning tokenizer-agnostic spans via their attention-weighted Center of Mass improves cross-tokenizer knowledge distillation.

Principles

• Distillation should transfer system dynamics, not just final outputs.
• Span-level alignment is more robust than token-level alignment.
• Semantically important spans should be weighted more heavily.

Method

SRA identifies aligned spans using Longest Common Subsequence, computes span representations via attention-weighted token pooling (Center of Mass), and distills knowledge through span-level hidden state transfer with a geometric regularizer and span logit distillation.

In practice

• Use character offsets to identify tokenizer-agnostic spans.
• Employ attention-weighted pooling for span representations.
• Apply a geometric regularizer to preserve representation space structure.

Topics

• Large Language Model Distillation
• Cross-Tokenizer Knowledge Distillation
• Span Representation Alignment
• Multi-Particle Dynamical Systems
• Center of Mass

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.