Summary of Dppa: Pruning Method For Large Language Model to Model Merging, by Yaochen Zhu et al.
DPPA: Pruning Method for Large Language Model to Model Merging
by Yaochen Zhu, Rui Xia, Jiajun Zhang
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 explores the concept of model merging, which combines fine-tuned models from multiple domains to enhance their proficiency across various domains. The primary challenge is resolving parameter conflicts during the merging process. Researchers have previously addressed this issue by modifying the merging stage, but this approach can be inefficient for complex fine-tuned models with significant parameter bias relative to baseline models. To overcome this limitation, the authors introduce Dynamic Pruning Partition Amplification (DPPA), a dual-stage method that initially applies Dynamically Pruning (DP) based on magnitude pruning and subsequently uses Dynamically Partition Amplification (DPA) to dynamically amplify parameter partitions according to their significance levels. The experimental results demonstrate that DPPA maintains only 20% of domain-specific parameters while achieving performance comparable to methods preserving up to 90% of parameters, with a notable improvement of nearly 20% in model merging post-pruning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have many models that are great at doing specific tasks. But what if you want one super-model that can do all these tasks well? That’s the idea behind “model merging.” The problem is, when you combine these models, they sometimes fight over which parameters to use. Some researchers have tried to fix this by adjusting how the models are combined. But this doesn’t work as well for more complex models. In this paper, scientists introduce a new approach called DPPA that tackles this challenge. They split their method into two parts: first, they “prune” or remove less important parameters; then, they amplify the importance of the remaining parameters. The results show that DPPA can achieve great performance while keeping most of the original models’ information. |
Keywords
» Artificial intelligence » Pruning
