Dual-Space Knowledge Distillation with Key-Query Matching for Large Language Models with Vocabulary Mismatch
Large language models (LLMs) achieve state-of-the-art (SOTA) performance across language tasks, but are costly to deploy due to their size and resource demands. Knowledge Distillation (KD) addresses this by training smaller Student models to mimic larger Teacher models, improving efficiency without significant performance loss. Dual-Space Knowledge Distillation with Cross-Model Attention (DSKD-CMA) has emerged as a SOTA method for KD between LLMs with distinct tokenizers, yet its internal workings remain largely opaque. In this work, we systematically analyse the attention mechanism of DSKD-CMA through manual token alignment probing and heatmap visualisations, revealing both strengths and...
| Authors | Stella Eva Tsiapali, Cong-Thanh Do, Kate Knill |
|---|---|
| Primary category | cs.CL |
| Published | 23 March 2026 |
| Last updated at source | 17 May 2026 |
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