4 papers · 1 filter
Knowledge Collapse in LLMs: When Fluency Survives but Facts Fail under Recursive Synthetic Training
Figarri Keisha, Zekun Wu, Ze Wang +2
Large language models increasingly rely on synthetic data due to human-written content scarcity, yet recursive training on model-generated outputs leads to model collapse, a degene…
MPF: Aligning and Debiasing Language Models post Deployment via Multi Perspective Fusion
Xin Guan, PeiHsin Lin, Zekun Wu +4
Multiperspective Fusion (MPF) is a novel posttraining alignment framework for large language models (LLMs) developed in response to the growing need for easy bias mitigation. Built…
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…
JobFair: A Framework for Benchmarking Gender Hiring Bias in Large Language Models
Ze Wang, Zekun Wu, Xin Guan +6
The use of Large Language Models (LLMs) in hiring has led to legislative actions to protect vulnerable demographic groups. This paper presents a novel framework for benchmarking hi…