The authors introduce DSKD-CMA-GA using generative adversarial learning to fix key-query distribution mismatches in cross-tokenizer knowledge distillation, reporting modest average ROUGE-L gains of 0.37 especially on out-of-distribution data.
Knowl- edge Distillation (KD) mitigates this by transferring capabilities from large Teacher models to smaller Student models, preserving performance while improving efficiency
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Dual-Space Knowledge Distillation with Key-Query Matching for Large Language Models with Vocabulary Mismatch
The authors introduce DSKD-CMA-GA using generative adversarial learning to fix key-query distribution mismatches in cross-tokenizer knowledge distillation, reporting modest average ROUGE-L gains of 0.37 especially on out-of-distribution data.