Summary of Random Masking Finds Winning Tickets For Parameter Efficient Fine-tuning, by Jing Xu et al.
Random Masking Finds Winning Tickets for Parameter Efficient Fine-tuning
by Jing Xu, Jingzhao Zhang
First submitted to arxiv on: 4 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper investigates parameter-efficient fine-tuning (PEFT) techniques for large language models (LLMs), aiming to reduce the computational costs associated with model training. Specifically, it explores the limits of PEFT by simplifying its design and reducing the number of trainable parameters. The researchers employ Random Masking, a surprisingly effective approach that matches the performance of standard PEFT algorithms such as LoRA on various tasks while using fewer trainable parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PEFT helps address the challenges of fine-tuning large language models by training only a fraction of the parameters. This technique is essential in exploring the expressiveness and flexibility of pre-trained models. The study demonstrates that Random Masking, despite its simplicity, can achieve comparable results to standard PEFT methods using fewer trainable parameters. |
Keywords
» Artificial intelligence » Fine tuning » Lora » Parameter efficient
