Summary of Softlms: Efficient Adaptive Low-rank Approximation Of Language Models Using Soft-thresholding Mechanism, by Priyansh Bhatnagar et al.
SoftLMs: Efficient Adaptive Low-Rank Approximation of Language Models using Soft-Thresholding Mechanism
by Priyansh Bhatnagar, Linfeng Wen, Mingu Kang
First submitted to arxiv on: 15 Nov 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 proposes a novel compression methodology for attention-based language models, aiming to reduce computational and memory overheads while maintaining performance. The method dynamically determines the rank of each layer using a soft thresholding mechanism, allowing for optimal compression decisions. This approach is applied to BERT, GPT2, and TinyLlama for discriminative and generative tasks, as well as Mamba, a state-space model. Experiments demonstrate a speed-up of 1.33X to 1.72X in the encoder/decoder with a 50% reduction in total parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make language models run faster on devices that don’t have much power or memory. To do this, they came up with a new way to shrink down some of the big parts inside these models without losing any information. They tested it on some well-known language models and found that it made them work 30-70% faster while still keeping all their abilities. |
Keywords
» Artificial intelligence » Attention » Bert » Encoder decoder
