2 papers
cs.AI2025
Bias Amplification: Large Language Models as Increasingly Biased Media
Ze Wang, Zekun Wu, Jeremy Zhang +5
Model collapse, a phenomenon characterized by performance degradation due to iterative training on synthetic data, has been widely studied. However, its implications for bias ampli…
cs.CL2025
SAGED: A Holistic Bias-Benchmarking Pipeline for Language Models with Customisable Fairness Calibration
Xin Guan, Ze Wang, Nathaniel Demchak +5
The development of unbiased large language models is widely recognized as crucial, yet existing benchmarks fall short in detecting biases due to limited scope, contamination, and l…