Summary of Characterizing the Accuracy — Efficiency Trade-off Of Low-rank Decomposition in Language Models, by Chakshu Moar et al.
Characterizing the Accuracy – Efficiency Trade-off of Low-rank Decomposition in Language Models
by Chakshu Moar, Faraz Tahmasebi, Michael Pellauer, Hyoukjun Kwon
First submitted to arxiv on: 10 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 paper investigates the accuracy-efficiency trade-off of low-rank decomposition methods, specifically Tucker decomposition, on recent large language models (LLMs). LLMs are optimized for broad problem-solving capabilities but are memory-bound due to matrix operations. Model compression techniques like quantization and pruning have been explored to optimize memory footprint and traffic. This work formalizes the design space for low-rank decomposition and conducts case studies on six widely used benchmarks for BERT and Llama 2 models. The results show that a 9% model size reduction can be achieved with minimal accuracy drops (4-10 percentage points) without retraining, making low-rank decomposition a promising direction for real-time LLM-based applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at ways to make big language models more efficient. These models are super powerful but use up lots of memory and processing power. The researchers want to find the best way to shrink these models without losing their ability to solve problems. They try a technique called low-rank decomposition, which can reduce the size of the model by 9% with only a small loss in accuracy (about 4-10 percentage points). This could be useful for applications that need fast and accurate language processing, such as AI assistants or real-time coding tools. |
Keywords
» Artificial intelligence » Bert » Llama » Model compression » Pruning » Quantization
