Summary of Lora Meets Dropout Under a Unified Framework, by Sheng Wang et al.
LoRA Meets Dropout under a Unified Framework
by Sheng Wang, Liheng Chen, Jiyue Jiang, Boyang Xue, Lingpeng Kong, Chuan Wu
First submitted to arxiv on: 25 Feb 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 This paper investigates the relationship between large language models (LLMs) and parameter-efficient finetuning methods, specifically LoRA. It highlights a potential contradiction between the negligible trainable parameters of LoRA and the effectiveness of previous dropout methods in alleviating overfitting. To address this gap, the authors confirm that LoRA is also overfitting-prone and revisit transformer-specific dropout methods to establish their equivalence and distinctions mathematically and empirically. They introduce a unified framework for investigating these methods, which reveals new preferences and performance comparisons when involving limited trainable parameters. The paper concludes by introducing a novel dropout method named HiddenKey, which exhibits remarkable superiority and sufficiency across multiple models and tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how big language models can be fine-tuned to work better without using too many extra parameters. It shows that a popular way of doing this called LoRA is actually prone to overfitting, which means it gets too good at recognizing patterns in the training data and doesn’t generalize well to new data. The authors also look at different dropout methods that help prevent overfitting and show how they work together with LoRA. They create a framework for studying these methods and find that one method called HiddenKey is particularly effective and efficient. |
Keywords
» Artificial intelligence » Dropout » Lora » Overfitting » Parameter efficient » Transformer
