Mixed batching with only 10% target-domain speech achieves word error rates matching or exceeding conventional full-dataset ASR fine-tuning in LLM-based models.
arXiv preprint arXiv:2005.04290 (2020)
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This survey paper identifies opportunities for LLMs in low-resource language humanities research along with challenges in data accessibility, model adaptability, and cultural sensitivity.
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Closing the Speech-Text Gap with Limited Audio for Effective Domain Adaptation in LLM-Based ASR
Mixed batching with only 10% target-domain speech achieves word error rates matching or exceeding conventional full-dataset ASR fine-tuning in LLM-based models.
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Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research
This survey paper identifies opportunities for LLMs in low-resource language humanities research along with challenges in data accessibility, model adaptability, and cultural sensitivity.