Summary of Not All Layers Of Llms Are Necessary During Inference, by Siqi Fan et al.
Not All Layers of LLMs Are Necessary During Inference
by Siqi Fan, Xin Jiang, Xiang Li, Xuying Meng, Peng Han, Shuo Shang, Aixin Sun, Yequan Wang, Zhongyuan Wang
First submitted to arxiv on: 4 Mar 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 This paper proposes a novel algorithm called AdaInfer to optimize the inference phase of Large Language Models (LLMs). The authors analyze the behavior of LLMs and find that not all layers are necessary for certain tasks. By predicting when intermediate layer results match the final output, they can significantly reduce the inference cost. AdaInfer relies on statistical features and classic classifiers like SVM to adaptively terminate the inference process. Experimental results show that AdaInfer achieves an average pruning ratio of 17.8% with nearly no performance drop (<1%) on well-known LLMs such as the Llama2 series and OPT, maintaining generalizability across tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper explores ways to make Large Language Models (LLMs) work more efficiently. The scientists found that some tasks can be done with less processing power than others. They created an algorithm called AdaInfer that predicts when the LLMs don’t need to use all their layers to get accurate results. This can save a lot of time and energy. The researchers tested AdaInfer on different LLMs and found it could reduce the processing needed by up to 43% without affecting the accuracy. |
Keywords
* Artificial intelligence * Inference * Pruning
