Summary of Accelerating Large Language Model Inference with Self-supervised Early Exits, by Florian Valade
Accelerating Large Language Model Inference with Self-Supervised Early Exits
by Florian Valade
First submitted to arxiv on: 30 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Machine Learning (stat.ML)
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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 technique accelerates inference in large language models (LLMs) by introducing early exits during the process. The approach capitalizes on the variability in token complexity to selectively accelerate inference. Early exit “heads” are integrated atop transformer layers, facilitating conditional terminations based on a confidence metric. These heads are trained self-supervised using the model’s own predictions, eliminating the need for additional annotated data. The method reduces computational time while preserving accuracy on certain tasks, leveraging existing knowledge in pre-trained LLMs without requiring retraining. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to make large language models work faster by stopping them early when they’re confident enough. The model is trained to know when it’s done, using its own predictions as training data. This makes it more efficient and can be used in applications like real-time language processing where speed matters. |
Keywords
» Artificial intelligence » Inference » Self supervised » Token » Transformer
