Summary of Cross-architecture Transfer Learning For Linear-cost Inference Transformers, by Sehyun Choi
Cross-Architecture Transfer Learning for Linear-Cost Inference Transformers
by Sehyun Choi
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Cross-Architecture Transfer Learning (XATL) method leverages shared components between Linear-Cost Inference (LCI) and self-attention-based transformers, such as layer norms, MLPs, input/output embeddings, to directly transfer pre-trained model parameters. This approach reduces training time up to 2.5x times and converges to a better minimum with up to 2.6% stronger models on language modeling benchmarks within the same compute budget. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to make language models more efficient by sharing components between different architectures. It’s like using pre-trained blocks to build new models, which saves time and gets better results. This method is tested on various sizes and types of attention mechanisms, showing it can be up to 2.5 times faster and 2.6% stronger. |
Keywords
» Artificial intelligence » Attention » Inference » Self attention » Transfer learning
