Summary of Angofa: Leveraging Ofa Embedding Initialization and Synthetic Data For Angolan Language Model, by Osvaldo Luamba Quinjica et al.
ANGOFA: Leveraging OFA Embedding Initialization and Synthetic Data for Angolan Language Model
by Osvaldo Luamba Quinjica, David Ifeoluwa Adelani
First submitted to arxiv on: 3 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 This paper addresses the gap in pre-trained language models for very-low resource languages, specifically Angolan languages. The Multilingual Adaptive Fine-tuning (MAFT) approach is used to finetune four tailored PLMs, which outperform existing models like AfroXLMR-base and OFA by 12.3 and 3.8 points respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps fill a gap in language models for languages with limited resources. The researchers create special language models just for Angolan languages using an approach called Multilingual Adaptive Fine-tuning (MAFT). These models do better than others like AfroXLMR-base and OFA. |
Keywords
» Artificial intelligence » Fine tuning
