arXiv Machine Learning

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.

arXiv Machine Learning

Jun 9

Exposing Hidden Biases in Text-to-Image Models via Automated Prompt Search

arXiv:2512. 08724v3 Announce Type: replace Abstract: Text-to-image (TTI) diffusion models have achieved remarkable visual quality, yet they have been repeatedly shown to exhibit social biases across sensitive attributes such as gender, race and age.

By Manos Plitsis, Giorgos Bouritsas, Vassilis Katsouros, Yannis Panagakis

llms 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 Machine Learning

Jun 2

KG-FairDiff: Knowledge Graph-Guided Prompt Refinement for Demographically Fair Text-to-Image Generation

arXiv:2606. 01282v1 Announce Type: cross Abstract: Text-to-Image (TTI) systems are now everyday infrastructure for journalism, education, advertising, and public communication, and the demographic and cultural stereotypes they inherit from training data (rendering women, people of colour, older adults, and non-Western cultures as under-represented or caricatured) become a population-level harm at deployment scale.

By Farbod Davoodi, Seyed Reza Tavakoli Shiyadeh, Pooria Safaei, Sana Harighi, Parsa Gholami, Amirali Amini, Kimia Vanaei, Emad Firoozi, Parham Abed Azad, Babak Khalaj, Siavash Ahmadi, Amir Hossein Payberah, Mohammad Hossein Rohban, Soheil Kolouri, Ali Diba

llms diffusion safety

arXiv AI

Jul 20

Debiasing Text-to-Image Evaluation via Implicit Cultural Alignment Reward Modeling

arXiv:2607. 15740v1 Announce Type: cross Abstract: As Text-to-Image (T2I) systems rapidly advance, evaluating the cultural authenticity of synthesized content has become increasingly important for fair and trustworthy generative AI.

By Bo-An Chang, Yu-Chih Chen

llms diffusion nlp reinforcement-learning multimodal benchmarks safety

arXiv AI

Jul 10

Adaptive Generation of Bias-Eliciting Questions for LLMs

arXiv:2510. 12857v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are now widely deployed in user-facing applications, reaching hundreds of millions of users worldwide.

By Robin Staab, Jasper Dekoninck, Maximilian Baader, Martin Vechev

llms benchmarks safety

arXiv AI

Jun 19

DeFrame: Debiasing Large Language Models Against Framing Effects

arXiv:2602. 04306v2 Announce Type: replace-cross Abstract: As large language models (LLMs) are increasingly deployed in real-world applications, ensuring their fair responses across demographics has become crucial.

By Kahee Lim, Soyeon Kim, Steven Euijong Whang

llms benchmarks safety

arXiv AI

Jul 2

The Illusion of High Utility in Safety Alignment of Text-to-Image Diffusion Models

arXiv:2607. 00402v1 Announce Type: cross Abstract: Safety alignment of text-to-image (T2I) diffusion models aims to suppress harmful generations while preserving utility on benign prompts.

By Adeel Yousaf, Soumik Ghosh, James Beetham, Amrit Singh Bedi, Mubarak Shah

rag diffusion nlp benchmarks safety

arXiv AI

1d ago

DR.GAP: Mitigating Bias in Large Language Models using Gender-Aware Prompting with Decoupled Reasoning

arXiv:2502. 11603v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) exhibit strong natural language understanding capabilities but also inherit and amplify societal biases, particularly gender bias, raising fairness concerns.

By Hongye Qiu, Yue Xu, Yi Wang, Meikang Qiu, Wenjie Wang

llms multimodal safety

arXiv AI

Aug 7

Once a Response, Always a Response: Detecting LLM-generated Text via Latent Prompt Restoration

arXiv:2608. 05741v1 Announce Type: cross Abstract: Large language models (LLMs) can generate fluent and convincing text at scale, creating growing risks for misinformation dissemination, educational misuse, and platform governance.

By Hongrui Bao, Yubing Ren, Yanan Cao, Jinhan You, Fang Fang, Shi Wang

llms benchmarks

arXiv AI

Jun 2

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts?

arXiv:2505. 16915v3 Announce Type: replace-cross Abstract: While recent Text-to-Image (T2I) models show impressive capabilities in synthesizing images from brief descriptions, they struggle with the long, detailed prompts required for professional applications.

By Qirui Jiao, Daoyuan Chen, Yilun Huang, Xika Lin, Ying Shen, Yaliang Li

diffusion benchmarks

arXiv AI

Jul 1

Arena-T2I Hard: Benchmarking and Improving Faithfulness with Dependency-Aware Checklist

arXiv:2606. 31711v1 Announce Type: new Abstract: Faithfulness -- how precisely a generated image aligns with its prompt -- is increasingly central to the real-world utility of text-to-image (T2I) models.

By Yuanhao Ban, Tong Xie, Sohyun An, Yunqi Hong, Evan Frick, I-Hung Hsu, Wei-Lin Chiang, Ion Stoica, Cho-Jui Hsieh

diffusion multimodal benchmarks

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

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