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cs.CL2024★ 5 cited
On the Diversity of Synthetic Data and its Impact on Training Large Language Models
Hao Chen, Abdul Waheed, Xiang Li +4
The rise of Large Language Models (LLMs) has accentuated the need for diverse, high-quality pre-training data. Synthetic data emerges as a viable solution to the challenges of data…
cs.CL2024
What Do Speech Foundation Models Not Learn About Speech?
Abdul Waheed, Hanin Atwany, Bhiksha Raj +1
Understanding how speech foundation models capture non-verbal cues is crucial for improving their interpretability and adaptability across diverse tasks. In our work, we analyze se…
cs.CL2024
uDistil-Whisper: Label-Free Data Filtering for Knowledge Distillation in Low-Data Regimes
Abdul Waheed, Karima Kadaoui, Bhiksha Raj +1
Recent work on distilling Whisper's knowledge into small models using pseudo-labels shows promising performance while reducing the size by up to 50%. This results in small, efficie…