Summary of Spp: Sparsity-preserved Parameter-efficient Fine-tuning For Large Language Models, by Xudong Lu et al.
SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models
by Xudong Lu, Aojun Zhou, Yuhui Xu, Renrui Zhang, Peng Gao, Hongsheng Li
First submitted to arxiv on: 25 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 The proposed SPP method is a Sparsity-Preserved Parameter-efficient fine-tuning technique designed to overcome the challenges posed by the immense sizes of Large Language Models (LLMs). Unlike existing post-training pruning methods, SPP maintains the original performance of LLMs by employing lightweight learnable column and row matrices to optimize sparse LLM weights. The method ensures consistency in model sparsity pattern and ratio during both training and weight-merging processes through element-wise multiplication and residual addition. This approach is demonstrated to be effective for various LLM models, including the LLaMA and LLaMA-2 families, with different sparsity patterns and ratios. Specifically, SPP enhances the performance of models with high sparsity ratios (e.g., 75%), making it a promising solution for efficient fine-tuning of sparse LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SPP is a new way to make large language models smaller without losing their power. The problem is that these huge models are hard to train and use because they take up too much space. SPP solves this by using special lightweight matrices to help the model forget some information, keeping only what’s important. This makes the model smaller but still good at understanding and generating text. It works well with different types of language models and even helps them get better at tasks like chatting or writing stories. |
Keywords
» Artificial intelligence » Fine tuning » Llama » Parameter efficient » Pruning
