Hugging Face Trending Papers July 9, 2026

Beyond wheelchairs and blindfolds: Investigating disability stereotypes in T2I models with INCLUDE-BENCH

Read the original on Hugging Face Trending Papers →

Text-to-image (T2I) models have been shown to exhibit social biases. Prior work has mainly focused on gender, skin tone, and cultural representation within restricted occupational associations, and emerging benchmarks increasingly incorporate these dimensions.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

arXiv AI

Jun 18

When Cars Have Stereotypes: Auditing Demographic Bias in Objects from Text-to-Image Models

arXiv:2508. 03483v3 Announce Type: replace-cross Abstract: While prior research on text-to-image generation has predominantly focused on biases in human depictions, demographic bias in generated objects remains relatively underexplored.

By Dasol Choi, Jihwan Lee, Minjae Lee, Minsuk Kahng

diffusion benchmarks safety

arXiv AI

Jul 31

BioPro: Towards Difference-Aware Gender Fairness for Vision-Language Models

arXiv:2512. 00807v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) inherit significant social biases from their training data, notably in gender representation.

By Yujie Lin, Jiayao Ma, Qingguo Hu, Wenbo Li, Genji Li, Derek Wong, Jinsong Su

llms rag diffusion multimodal safety

arXiv AI

Jun 2

Prototypicality Bias Reveals Blindspots in Multimodal Evaluation Metrics

arXiv:2601. 04946v3 Announce Type: replace-cross Abstract: Automatic metrics are widely used to evaluate text-to-image models, often replacing human judgment in benchmarking, model selection, and large-scale data filtering.

By Subhadeep Roy, Gagan Bhatia, Steffen Eger

rag diffusion multimodal benchmarks safety

arXiv Machine Learning

Jun 15

Aligned but Stereotypical? How System Prompts Shape Demographic Bias in LLM-Based Text-to-Image Models

arXiv:2512. 04981v2 Announce Type: replace-cross Abstract: Text-to-image (T2I) systems increasingly rely on Large Language Model (LLM)-based text conditioning to interpret and expand user prompts.

By NaHyeon Park, Na Min An, Kunhee Kim, Soyeon Yoon, Jiahao Huo, Hyunjung Shim

llms rag diffusion benchmarks safety

arXiv AI

Jul 31

Identifying Implicit Bias in LLM-based Chat AI Toward People with Intellectual Disabilities

arXiv:2607. 26062v1 Announce Type: cross Abstract: Background: This work investigates the presence of implicit bias in Large Language Model (LLM)-based chat AI models directed toward people with intellectual disabilities (ID).

By Karly V. Coffey, Gloria L. Krahn, John P. Hanley, Jacob E. Neely

llms safety

arXiv AI

Jul 24

ImplicitBBQ: Benchmarking Implicit Bias in Large Language Models through Characteristic Based Cues

arXiv:2604. 01925v2 Announce Type: replace-cross Abstract: Large Language Models increasingly suppress biased outputs when demographic identity is stated explicitly, yet may still exhibit implicit biases when identity is conveyed indirectly.

By Bhaskara Hanuma Vedula, Darshan Anghan, Ishita Goyal, Ponnurangam Kumaraguru, Abhijnan Chakraborty

llms benchmarks safety

Related stories

When Cars Have Stereotypes: Auditing Demographic Bias in Objects from Text-to-Image Models

BioPro: Towards Difference-Aware Gender Fairness for Vision-Language Models

Prototypicality Bias Reveals Blindspots in Multimodal Evaluation Metrics

Aligned but Stereotypical? How System Prompts Shape Demographic Bias in LLM-Based Text-to-Image Models

Identifying Implicit Bias in LLM-based Chat AI Toward People with Intellectual Disabilities

ImplicitBBQ: Benchmarking Implicit Bias in Large Language Models through Characteristic Based Cues