Summary of Fisher Mask Nodes For Language Model Merging, by Thennal D K et al.
Fisher Mask Nodes for Language Model Merging
by Thennal D K, Ganesh Nathan, Suchithra M S
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 pre-trained language models, such as BERT and its derivatives, has been shown to significantly improve performance on downstream tasks. However, when dealing with multiple tasks, task-specific fine-tuned models typically perform well only on one task and require additional training or ensembling for multi-task scenarios. To address this challenge, researchers have proposed the concept of model merging, which combines multiple task-specific models into a single multi-task model. In this study, we introduce a novel model merging method for Transformers that utilizes Fisher information to combine insights from previous work in Fisher-weighted averaging and model pruning. Our method, which leverages the Fisher information of mask nodes within the Transformer architecture, is computationally efficient and outperforms full-scale Fisher-weighted averaging with significant performance improvements up to +6.5 and speedups between 57.4x and 321.7x across models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making language models work better together. Right now, we have many different language models that can do one task really well, but they don’t work as well when doing multiple tasks at the same time. To fix this, scientists are trying to combine these models into a single model that can handle many tasks. The researchers in this study came up with a new way to do this using something called Fisher information. Their method is fast and works really well, improving performance by up to 6.5% and making it faster than previous methods. |
Keywords
* Artificial intelligence * Bert * Fine tuning * Mask * Multi task * Pruning * Transformer
