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Ludie Guo

2 accepted papers

2025

Bridging the Fairness Gap: Enhancing Pre-trained Models with LLM-Generated Sentences

ICASSP 2025accepted

Pre-trained language models (PLMs) are trained on data that inherently contains gender biases, leading to undesirable impacts. Traditional debiasing methods often rely on external corpora, which may lack quality, diversity, or demographic balance, affecting the effectiveness of debiasing. With the r…

Cited by 0SourceScholar
2024

Biases Mitigation and Expressiveness Preservation in Language Models: A Comprehensive Pipeline (Student Abstract)

AAAI 2024technical

Pre-trained language models (PLMs) have greatly transformed various downstream tasks, yet frequently display social biases from training data, raising fairness concerns. Recent efforts to debias PLMs come with limitations: they either fine-tune the entire parameters in PLMs, which is time-consuming…

Cited by 3SourcePDFScholar