Summary of Sharelora: Parameter Efficient and Robust Large Language Model Fine-tuning Via Shared Low-rank Adaptation, by Yurun Song et al.
ShareLoRA: Parameter Efficient and Robust Large Language Model Fine-tuning via Shared Low-Rank Adaptation
by Yurun Song, Junchen Zhao, Ian G. Harris, Sangeetha Abdu Jyothi
First submitted to arxiv on: 16 Jun 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 study presents an innovative approach to optimize fine-tuning for Pretrained Language Models (PLMs) by leveraging a Shared Low Rank Adaptation (ShareLoRA) technique. The method reduces the number of training parameters and memory usage while maintaining model performance in both classification and generation tasks across various models, including RoBERTa, GPT-2, LLaMA, and LLaMA2. ShareLoRA demonstrates superior transfer learning capabilities compared to standard LoRA applications and mitigates overfitting by sharing weights across layers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows a new way to make language models better and more efficient. It uses a special technique called Shared Low Rank Adaptation (ShareLoRA) that helps reduce the amount of data needed to train these models while keeping their performance strong. ShareLoRA works well with different types of language models, including RoBERTa, GPT-2, LLaMA, and LLaMA2. This means it can be used for a wide range of tasks, from simple text classification to more complex tasks like generating new text. |
Keywords
» Artificial intelligence » Classification » Fine tuning » Gpt » Llama » Lora » Low rank adaptation » Overfitting » Text classification » Transfer learning
