Summary of Gelora: Geometric Adaptive Ranks For Efficient Lora Fine-tuning, by Abdessalam Ed-dib et al.
GeLoRA: Geometric Adaptive Ranks For Efficient LoRA Fine-tuning
by Abdessalam Ed-dib, Zhanibek Datbayev, Amine Mohamed Aboussalah
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Geometric Topology (math.GT); Machine Learning (stat.ML)
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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 Fine-tuning large language models (LLMs) is computationally expensive because it involves updating all parameters. LoRA (Low-Rank Adaptation) improves efficiency by modifying only a subset of weights, but this comes with a trade-off between expressivity and computational cost: lower ranks reduce resources but limit expressiveness, while higher ranks enhance expressivity at increased cost. The authors propose GeLoRA (Geometric Low-Rank Adaptation), a framework that computes the intrinsic dimensionality of hidden state representations to adaptively select LoRA ranks. This allows for a principled selection that balances efficiency and expressivity. GeLoRA dynamically adjusts the rank for each layer based on the intrinsic dimensionality of its input and output representations, recognizing that not all model parameters equally impact fine-tuning. The authors demonstrate that GeLoRA consistently outperforms recent baselines within the same parameter budget. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making large language models more efficient without sacrificing their ability to learn new things. One way to do this is by using a technique called LoRA (Low-Rank Adaptation). However, LoRA has its own set of problems: it can make the model less accurate or take up too many resources. The authors propose a new approach called GeLoRA that figures out how much to adjust each part of the model based on what it’s doing. This helps GeLoRA find the right balance between being efficient and being good at learning. In tests, GeLoRA performed better than other methods while using the same amount of resources. |
Keywords
» Artificial intelligence » Fine tuning » Lora » Low rank adaptation
