arXiv AI October 28, 2026 By Zhipeng Yin, Zichong Wang, Avash Palikhe, Wenbin Zhang

Fairness Definitions in Language Models Explained

The paper "Fairness Definitions in Language Models Explained" surveys and clarifies various fairness notions applicable to language models (LMs). It introduces a taxonomy categorizing these concepts by transformer architecture—encoder-only, decoder-only, and encoder-decoder—and demonstrates each definition through experiments to illustrate practical implications. The authors also discuss current research challenges and open questions to encourage further innovation in the field.

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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

Sep 25

Fair Like Us? Auditing LLM Alignment in Resource Allocation

The paper "Fair Like Us? Auditing LLM Alignment in Resource Allocation" presents a method for evaluating how large language models reason about fairness in the allocation of scarce, indivisible resources. It compares LLMs’ first‑person fairness judgments with human responses across various scenarios, finding that models tend to favor stricter fairness constraints, exhibit more self‑interested behavior, and are sensitive to framing. The study also shows that current fine‑tuning datasets struggle to align LLM judgments with human ones.

By Qishen Han, Hadi Hosseini, Joshua Kavner, Samarth Khanna, Sujoy Sikdar, Lirong Xia

llms agents fine-tuning safety

arXiv Computation and Language

Sep 15

Intersectional Fairness in Large Language Models

arXiv:2604.20677v3 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly deployed in socially sensitive settings, raising concerns about fairness and bias, particularly when...

By Chaima Boufaied, Ronnie De Souza Santos, Ann Barcomb

llms nlp benchmarks safety

arXiv Computation and Language

Oct 9

Fair-GPTQ: Bias-Aware Quantization for Large Language Models

Fair-GPTQ is a quantization method for large language models that incorporates explicit group‑fairness constraints into the quantization objective. By guiding the rounding operation, it reduces bias in generated text, particularly for occupational stereotypes and discriminatory language across gender, race, and religion. The approach maintains at least 90 % of baseline accuracy on zero‑shot benchmarks while preserving the memory and speed benefits of 4‑bit quantization.

By Irina Proskurina, Guillaume Metzler, Julien Velcin

llms efficiency 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 Machine Learning

Sep 21

FairLMs: A Turnkey Library for Fairness in Language Models

FairLMs is a Python library designed to streamline fairness research in language models by unifying bias measurement, mitigation, and evaluation evidence. It offers 33 intrinsic and extrinsic metrics, 14 mitigation components across four intervention categories, 14 diagnostic tools, adapters for major Transformer architectures and hosted APIs, and benchmark loaders. The library enforces explicit declarations of model capabilities and input requirements, ensuring compatibility and reproducibility across components and datasets.

By Jiale Zhang, Michael Larionov, Zichong Wang, Zhipeng Yin, Wenbin Zhang