Summary of Mitigating Language-level Performance Disparity in Mplms Via Teacher Language Selection and Cross-lingual Self-distillation, by Haozhe Zhao et al.
Mitigating Language-Level Performance Disparity in mPLMs via Teacher Language Selection and Cross-lingual Self-Distillation
by Haozhe Zhao, Zefan Cai, Shuzheng Si, Liang Chen, Yufeng He, Kaikai An, Baobao Chang
First submitted to arxiv on: 12 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes ALSACE, a method to mitigate language-level performance disparities in multilingual Pretrained Language Models (mPLMs) without requiring additional labeled data. It leverages the learned knowledge from well-performing languages within an mPLM to guide under-performing ones, eliminating the need for fine-tuning with limited labeled data. The approach demonstrates competitive performance on various multilingual NLU tasks in both full-resource and limited-resource settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a way to make language models work better across different languages without needing lots of new training data. It’s like giving hints to the model when it struggles to understand certain languages, helping it perform better overall. This method can be useful for many natural language processing tasks and might help us build more powerful language models in the future. |
Keywords
» Artificial intelligence » Fine tuning » Natural language processing
