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arXiv CS.AI·28d ago·5 min read
Multi-Task Instruction Tuning via Data Scheduling for Low-Resource Arabic SpeechLLMs
TL;DR
Low-resource Arabic speech models face challenges in instruction tuning due to limited data. A novel data scheduling approach was developed to enhance multi-task instruction tuning.
✦ Why It Matters
Engineers can implement data scheduling techniques to improve the performance of their low-resource language models immediately.
Key Takeaways
- Explore data scheduling to optimize training for low-resource languages.
- Implement multi-task instruction tuning to enhance model performance.
- Evaluate performance metrics to benchmark improvements in SpeechLLMs.
- Consider the implications of this approach for other underrepresented languages.
Full Summary
Low-resource languages like Arabic often struggle with developing effective speech language models (SpeechLLMs) due to insufficient training data. To address this, a new approach called data scheduling was introduced, which optimally organizes training data for multi-task instruction tuning.
This method allows models to learn from various tasks simultaneously, improving their overall performance. Experiments showed that the proposed approach led to a notable increase in accuracy, with performance metrics surpassing previous benchmarks for Arabic SpeechLLMs.
The findings suggest that data scheduling can be a game-changer for low-resource language processing, enabling better model training with limited data. This research opens avenues for enhancing speech technology in underrepresented languages, making it more accessible and effective.