Summary of Dual-space Knowledge Distillation For Large Language Models, by Songming Zhang et al.
Dual-Space Knowledge Distillation for Large Language Models
by Songming Zhang, Xue Zhang, Zengkui Sun, Yufeng Chen, Jinan Xu
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed dual-space knowledge distillation (DSKD) framework unifies the output spaces of large language models (LLMs) for more effective knowledge transfer. By aligning representations through a cross-model attention mechanism, DSKD supports KD between any two LLMs regardless of their vocabularies. This outperforms current white-box KD methods and is compatible with various distance functions. The framework demonstrates significant improvements on task-agnostic instruction-following benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way to help large language models share knowledge with each other. This method, called dual-space knowledge distillation (DSKD), makes it easier for different models to learn from each other, even if they have different words or vocabulary. The authors tested DSKD and showed that it works better than the current methods used in this area. |
Keywords
» Artificial intelligence » Attention » Knowledge distillation
