5 papers
BiasIG: Benchmarking Multi-dimensional Social Biases in Text-to-Image Models
Hanjun Luo, Zhimu Huang, Haoyu Huang +5
Text-to-Image (T2I) generative models have revolutionized content creation, yet they inherently risk amplifying societal biases. While sociological research provides systematic cla…
DynamicNER: A Dynamic, Multilingual, and Fine-Grained Dataset for LLM-based Named Entity Recognition
Hanjun Luo, Yingbin Jin, Xinfeng Li +6
The advancements of Large Language Models (LLMs) have spurred a growing interest in their application to Named Entity Recognition (NER) methods. However, existing datasets are prim…
VersusDebias: Universal Zero-Shot Debiasing for Text-to-Image Models via SLM-Based Prompt Engineering and Generative Adversary
Hanjun Luo, Ziye Deng, Haoyu Huang +3
With the rapid development of Text-to-Image (T2I) models, biases in human image generation against demographic social groups become a significant concern, impacting fairness and et…
BIGbench: A Unified Benchmark for Evaluating Multi-dimensional Social Biases in Text-to-Image Models
Hanjun Luo, Haoyu Huang, Ziye Deng +6
Text-to-Image (T2I) generative models are becoming increasingly crucial due to their ability to generate high-quality images, but also raise concerns about social biases, particula…
FAIntbench: A Holistic and Precise Benchmark for Bias Evaluation in Text-to-Image Models
Hanjun Luo, Ziye Deng, Ruizhe Chen +1
The rapid development and reduced barriers to entry for Text-to-Image (T2I) models have raised concerns about the biases in their outputs, but existing research lacks a holistic de…