arXiv AI

BiasGym: A Simple and Generalizable Framework for Analyzing and Removing Biases through Injection

BiasGym is a cost‑effective, generalizable framework that injects specific biases into large language models via token‑based fine‑tuning while keeping the model frozen. It then uses two debiasing methods—Scope and Steer—to identify and suppress or redirect the components responsible for biased behavior. The framework enables consistent bias elicitation, precise localization of bias associations, and targeted debiasing without harming downstream performance, and it has been shown to reduce real‑world stereotypes such as labeling Italians as reckless drivers.

arXiv Machine Learning

Sep 3

GPTBIAS: A Comprehensive Framework for Evaluating Bias in Large Language Models

The paper introduces GPTBIAS, a framework that uses powerful large language models like GPT‑4 to evaluate bias in other LLMs. It employs specially crafted prompts called Bias Attack Instructions to probe for bias and outputs a bias score along with detailed information such as bias types, affected demographics, keywords, reasons, and improvement suggestions. Extensive experiments demonstrate the framework’s effectiveness and usability.

By Jiaxu Zhao, Meng Fang, Shirui Pan, Wenpeng Yin, Mykola Pechenizkiy

llms fine-tuning safety

arXiv AI

Jun 30

Knowing Bias, Doing Better: Mitigating Social Bias in LLMs via Know-Bias Neuron Enhancement

arXiv:2601. 21864v2 Announce Type: replace Abstract: Large language models (LLMs) exhibit social biases that reinforce harmful stereotypes, limiting their safe deployment.

By Jinhao Pan, Chahat Raj, Anjishnu Mukherjee, Sina Mansouri, Bowen Wei, Shloka Yada, Ziwei Zhu

llms benchmarks safety

arXiv AI

Oct 7

Latent space bias directions in LLMs capture confidence, not fairness

arXiv:2610.08559v1 Announce Type: cross Abstract: Activation steering has gained popularity as a lightweight inference-time debiasing technique for large language models. However, prior work reports...

By Stephanie Buttigieg, Maeve Madigan, Parameswaran Kamalaruban, Stuart Burrell

llms diffusion benchmarks safety

arXiv AI

Aug 17

BiasTrace: Linking Reasoning Behaviours to Biased Outputs in LLMs

arXiv:2608. 14161v1 Announce Type: new Abstract: LLMs exhibit social biases that can produce inaccurate and discriminatory inferences, posing risks in high-stakes applications.

By Varsha Ramineni, Hossein A. Rahmani, Jerome Ramos, Karin Sevegnani, Emine Yilmaz

llms safety

arXiv AI

4d ago

Unfair and Unsafe: Everyday Bias Triggers Emergent Misalignment

arXiv:2610.15961v1 Announce Type: cross Abstract: Emergent misalignment (EM) occurs when fine-tuning a language model to a narrow task makes it misaligned on unrelated ones. Because organizations now...

By Alex Mart\'i Guiu, Nicolas Zumarraga, Kevin O'Sullivan, Robert Jakob, Anna Hedstr\"om

llms agents fine-tuning safety

arXiv AI

Sep 1

General Phrase Debiaser: Debiasing Masked Language Models at a Multi-Token Level

arXiv:2311.13892v4 Announce Type: replace-cross Abstract: The social biases and unwelcome stereotypes revealed by pretrained language models are becoming obstacles to their application. Compared to n...

By Bingkang Shi, Xiaodan Zhang, Dehan Kong, Yulei Wu, Zongzhen Liu, Honglei Lyu, Longtao Huang

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

Hugging Face Trending Papers

Jun 17

The Wrong Kind of Right: Quantifying and Localizing Misfired Alignment in LLMs

Warning: This paper studies stereotypes and biases, and contains potentially disturbing examples, used for illustration purposes only. Our findings should not be interpreted as an argument against alignment.

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 Computation and Language

Sep 10

BTBR: A Bayesian-Theory-Driven Probabilistic-Fuzzy Framework for Implicit Bias Removal in Large Language Models

arXiv:2408.10608v2 Announce Type: replace Abstract: Large language models (LLMs) may encode biased associations from heterogeneous training corpora that are not immediately visible under ordinary pro...

By Yongxin Deng (University of Technology Sydney), Xiaoyu Tan (National University of Singapore), Jing Pan (Monash University), Ling Chen (University of Technology Sydney), Zhen Fang (University of Technology Sydney), Xihe Qiu (National University of Singapore)

llms safety

Hugging Face Trending Papers

Jul 23

QuantiBias: Benchmarking Quantization-Induced Bias in LLMs

Almost every large language model that reaches a broad audience is quantized: trained in full precision, then compressed for efficiency. This step is assumed harmless and its safety is rarely re-checked.

llms efficiency benchmarks safety

arXiv Computer Vision

Sep 1

Guardrail-Agnostic Societal Bias Evaluation in Large Vision-Language Models

arXiv:2608.29590v1 Announce Type: new Abstract: We propose a societal bias evaluation method for large vision-language models (LVLMs) in the era of strong safety guardrails. Existing benchmarks rely...

By Yusuke Hirota, Michael Ross Boone, Arun George Zachariah, Jibin Rajan Varghese, Yu-Chiang Frank Wang, Boyi Li, Ryo Hachiuma

llms multimodal benchmarks safety

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