Summary of Neat: Nonlinear Parameter-efficient Adaptation Of Pre-trained Models, by Yibo Zhong et al.
NEAT: Nonlinear Parameter-efficient Adaptation of Pre-trained Models
by Yibo Zhong, Haoxiang Jiang, Lincan Li, Ryumei Nakada, Tianci Liu, Linjun Zhang, Huaxiu Yao, Haoyu Wang
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The proposed paper, NEAT, presents a novel approach to fine-tuning pre-trained models, addressing the computational expense of updating all parameters. Fine-tuning is often the key to achieving state-of-the-art performance, but it can be resource-intensive when updating all model weights. Previous methods, such as LoRA, have attempted to address this issue by freezing pre-trained weights and introducing low-rank matrices. However, these approaches struggle to capture complex nonlinear dynamics and optimal optimization trajectories, resulting in a performance gap relative to full fine-tuning. NEAT, a nonlinear PEFT approach, employs a lightweight neural network to learn a nonlinear transformation of the pre-trained weights, enabling better approximation of cumulative weight updates. Theoretical analysis demonstrates that NEAT achieves greater efficiency than LoRA while maintaining equivalent expressivity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary NEAT is a new way to make computers smarter by fine-tuning their models. Currently, making these models work well requires updating all the model’s parameters, which takes a lot of computer power and time. A previous approach called LoRA tried to solve this problem by freezing some of the pre-trained weights and adding special matrices. However, LoRA had trouble capturing complex patterns in the data, leading to a gap in performance compared to fully fine-tuning the models. To fix this, NEAT uses a simple neural network to transform the pre-trained weights in a way that better matches how they would be updated if we were to fine-tune them from scratch. |
Keywords
» Artificial intelligence » Fine tuning » Lora » Neural network » Optimization
