Summary of Surgical Feature-space Decomposition Of Llms: Why, When and How?, by Arnav Chavan et al.
Surgical Feature-Space Decomposition of LLMs: Why, When and How?
by Arnav Chavan, Nahush Lele, Deepak Gupta
First submitted to arxiv on: 17 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Low-rank approximations of weight and feature spaces can boost deep learning model performance, whether by improving generalization or reducing inference latency. However, there is no consensus on when and why these approximations are helpful for large language models (LLMs). Our study empirically investigates the efficacy of weight and feature space decomposition in transformer-based LLMs. We show that surgical decomposition not only provides insights into compression-performance trade-offs but also enhances commonsense reasoning performance. Our analysis identifies specific network segments with intrinsic low-rank structures and explores implications for model bias. Our findings offer a novel perspective on optimizing LLMs, positioning low-rank approximation as both a performance enhancer and potential bias rectifier. We use transformer-based LLMs, specifically BERT and RoBERTa, and evaluate models on benchmarks like GLUE and SuperGLUE. Our code is available at GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores ways to make deep learning models work better by simplifying their internal structures. It focuses on a type of model called large language models (LLMs), which are great at understanding human language but can be slow and inaccurate. The researchers tested different methods for making these models simpler, and found that one approach – called surgical decomposition – can actually make the models better at understanding certain types of information. They also looked at how this simplification affects the way the models treat certain biases in language. Overall, the paper shows that simplifying deep learning models can be a powerful tool for improving their performance and fairness. |
Keywords
» Artificial intelligence » Bert » Deep learning » Generalization » Inference » Transformer
