Summary of Enhancing Zeroth-order Fine-tuning For Language Models with Low-rank Structures, by Yiming Chen et al.
Enhancing Zeroth-order Fine-tuning for Language Models with Low-rank Structures
by Yiming Chen, Yuan Zhang, Liyuan Cao, Kun Yuan, Zaiwen Wen
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes a new method for fine-tuning large language models (LLMs) called low-rank zeroth-order optimization (LOZO). The authors show that traditional first-order fine-tuning algorithms can be memory-intensive, especially when adapting LLMs for downstream applications. They introduce a novel algorithm that uses finite differences to estimate gradients, eliminating the need for storing activation values and reducing memory costs. The proposed method provides convergence guarantees by framing it as a subspace optimization method. The authors also demonstrate that LOZO can integrate with momentum techniques without incurring additional memory costs. Extensive experiments across various model sizes and tasks show that LOZO outperforms existing zeroth-order methods and approaches the performance of first-order algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LOZO is a new way to fine-tune large language models (LLMs) that uses less memory. Right now, it’s hard to adapt these big models for specific tasks because they need a lot of space to work. The authors came up with an idea to use small changes in the model’s output to estimate how well it will do on a task. This helps reduce the amount of memory needed. They also show that this method can be used with another technique called momentum, which helps the model move towards better solutions. Tests with different models and tasks showed that LOZO works really well and is almost as good as the usual way of fine-tuning. |
Keywords
» Artificial intelligence » Fine tuning » Optimization
