Multi-Task Instruction Tuning via Data Scheduling for Low-Resource Arabic SpeechLLMs
📰 ArXiv cs.AI
Improve Arabic speech LLMs with multi-task instruction tuning via data scheduling for better speech understanding and generation
Action Steps
- Implement multi-task instruction tuning for Arabic speech LLMs using data scheduling
- Train the model on a combination of generative and discriminative tasks
- Evaluate the model's performance on automatic speech recognition (ASR) and speech summarization tasks
- Fine-tune the model using Arabic-centric data to adapt to linguistically complex and dialect-rich settings
- Compare the results with single-task models to measure the improvement in performance
Who Needs to Know This
NLP engineers and researchers working on low-resource languages like Arabic can benefit from this approach to improve their speech LLMs
Key Insight
💡 Multi-task instruction tuning via data scheduling can improve the performance of Arabic speech LLMs on low-resource settings
Share This
Boost Arabic speech LLMs with multi-task instruction tuning!
Key Takeaways
Improve Arabic speech LLMs with multi-task instruction tuning via data scheduling for better speech understanding and generation
Full Article
Title: Multi-Task Instruction Tuning via Data Scheduling for Low-Resource Arabic SpeechLLMs
Abstract:
arXiv:2601.12494v3 Announce Type: replace-cross Abstract: Audio large language models (LLMs) enable unified speech understanding and generation, but adapting them to linguistically complex and dialect-rich settings such as Arabic-English remains challenging. We present a controlled study of multi-task instruction tuning for an Arabic-centric audio LLM across generative tasks, including automatic speech recognition (ASR) and speech and text summarization, as well as discriminative tasks, includin
Abstract:
arXiv:2601.12494v3 Announce Type: replace-cross Abstract: Audio large language models (LLMs) enable unified speech understanding and generation, but adapting them to linguistically complex and dialect-rich settings such as Arabic-English remains challenging. We present a controlled study of multi-task instruction tuning for an Arabic-centric audio LLM across generative tasks, including automatic speech recognition (ASR) and speech and text summarization, as well as discriminative tasks, includin
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