Summary of Sparsegrad: a Selective Method For Efficient Fine-tuning Of Mlp Layers, by Viktoriia Chekalina et al.
SparseGrad: A Selective Method for Efficient Fine-tuning of MLP Layers
by Viktoriia Chekalina, Anna Rudenko, Gleb Mezentsev, Alexander Mikhalev, Alexander Panchenko, Ivan Oseledets
First submitted to arxiv on: 9 Oct 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 The paper proposes a new selective parameter-efficient fine-tuning (PEFT) method called SparseGrad that enhances the performance of Transformer models while reducing memory-intensive processes. By transferring layer gradients to a sparse structure, only 1% of the layer’s elements remain significant, resulting in reduced updated parameters. The authors apply SparseGrad to fine-tune popular transformer-based models such as BERT, RoBERTa, and LLaMa-2 for natural language understanding (NLU) and question-answering tasks. Compared to state-of-the-art PEFT approaches like LoRA and MeProp, SparseGrad outperforms them while maintaining identical memory requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to make big computer models smaller so they use less computer power. Right now, making these models better takes up too much space in the computer’s memory. The authors created a new method called SparseGrad that works well for a type of model block that usually gets ignored. This block has half of the model’s parameters! By making the gradients of this block super thin, they reduce how many things need to be updated. They tested their method on some popular models like BERT and RoBERTa and it did better than others that are already good at doing this. |
Keywords
» Artificial intelligence » Bert » Fine tuning » Language understanding » Llama » Lora » Parameter efficient » Question answering » Transformer
