Constructs gender-perturbed Bangla classification benchmarks and proposes RandSymKL debiasing that reduces extrinsic gender bias in pretrained models.
arXiv preprint arXiv:2408.02237 (2024) 34
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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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Mitigating Extrinsic Gender Bias for Bangla Classification Tasks
Constructs gender-perturbed Bangla classification benchmarks and proposes RandSymKL debiasing that reduces extrinsic gender bias in pretrained 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.