Summary of Dynamic Layer Selection in Decoder-only Transformers, by Theodore Glavas et al.
Dynamic layer selection in decoder-only transformers
by Theodore Glavas, Joud Chataoui, Florence Regol, Wassim Jabbour, Antonios Valkanas, Boris N. Oreshkin, Mark Coates
First submitted to arxiv on: 26 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 abstract discusses optimizing Large Language Models (LLMs) for efficient inference, specifically focusing on two dynamic inference methods: layer skipping and early exiting. Researchers examine the effectiveness of these approaches in natural language generation (NLG), concluding that a pre-trained decoder-only model is more robust to layer removal via layer skipping than early exit. The study also explores using hidden state information to adapt computation for layer skipping, demonstrating its challenges. Finally, the authors propose dynamic computation allocation on a per-sequence basis, achieving significant efficiency gains while maintaining equal performance to the full model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve the efficiency of large language models by adapting their architecture during inference. The study compares two methods for optimizing inference: layer skipping and early exiting. Results show that one method is more effective than the other in reducing computational cost. The researchers also explore using hidden state information to adapt computation, but find it challenging. The paper suggests a new approach to dynamic computation allocation, which achieves significant efficiency gains while maintaining performance. |
Keywords
» Artificial intelligence » Decoder » Inference
