Summary of Bilora: a Bi-level Optimization Framework For Overfitting-resilient Low-rank Adaptation Of Large Pre-trained Models, by Rushi Qiang et al.
BiLoRA: A Bi-level Optimization Framework for Overfitting-Resilient Low-Rank Adaptation of Large Pre-trained Models
by Rushi Qiang, Ruiyi Zhang, Pengtao Xie
First submitted to arxiv on: 19 Mar 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 paper introduces BiLoRA, a novel approach for fine-tuning large-scale pre-trained models in downstream tasks that alleviates overfitting. Building upon the popular LoRA method, BiLoRA employs bi-level optimization to parameterize low-rank incremental matrices, splitting training across two subsets of data to mitigate overfitting risks. The approach is tested on ten datasets covering natural language understanding and generation tasks, using various well-known large pre-trained models. Results show significant improvements over LoRA methods and other fine-tuning approaches, while maintaining similar trainable parameter amounts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BiLoRA is a new way to make big AI models better for specific jobs. The problem with current methods is they often get too good at the job training data and don’t do well on new, unseen data. BiLoRA tries to solve this by dividing the training into two parts: one for the “big picture” and another for the details. This helps the model learn more general patterns without getting stuck on a single dataset. The results show that BiLoRA works better than other methods and is useful for many different tasks, like language understanding and generation. |
Keywords
* Artificial intelligence * Fine tuning * Language understanding * Lora * Optimization * Overfitting
