arXiv AI June 19, 2026 By Kahee Lim, Soyeon Kim, Steven Euijong Whang

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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 Machine Learning

Aug 10

Let's Unlearn Stereotypes Before Decision-Making: Assessing the Impact of Intrinsic Bias Mitigation on Downstream Fairness in LLMs

arXiv:2509. 16462v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used in high-stakes decision-making systems, where biased predictions can reinforce social and economic disparities.

By Mina Arzaghi, Alireza Dehghanpour Farashah, Florian Carichon, Jean-Fran\c{c}ois Plante, Golnoosh Farnadi

llms rag fine-tuning benchmarks safety

arXiv AI

Jul 1

Wait, am I Being Fair? Characterizing Deductive Stereotyping and Mitigating It with Fair-GCG

arXiv:2606. 30989v1 Announce Type: cross Abstract: Warning: This paper contains several toxic and offensive statements.

By Naihao Deng, Yilun Zhu, Joan Nwatu, Clayton Scott, Rada Mihalcea

llms benchmarks safety

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 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

Jun 4

PoliticsBench: Benchmarking Political Values in Large Language Models with Multi-Turn Roleplay

arXiv:2603. 23841v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) are increasingly used as primary sources of information, their potential for political bias may impact their objectivity.

By Rohan Khetan, Ashna Khetan

llms benchmarks safety

Related stories

Adaptive Generation of Bias-Eliciting Questions for LLMs

Let's Unlearn Stereotypes Before Decision-Making: Assessing the Impact of Intrinsic Bias Mitigation on Downstream Fairness in LLMs

Wait, am I Being Fair? Characterizing Deductive Stereotyping and Mitigating It with Fair-GCG

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

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

PoliticsBench: Benchmarking Political Values in Large Language Models with Multi-Turn Roleplay