The paper introduces AttriBench, a benchmark dataset that balances author fame and demographics to study quote attribution in large language models (LLMs). Using AttriBench, the authors evaluate 11 popular LLMs and find that accurate attribution remains difficult, with significant disparities across race, gender, and intersectional groups. They also identify a new failure mode—suppression—where models omit attribution entirely, which is unevenly distributed across demographics and not reflected by standard accuracy metrics.
By Eliza Berman, Bella Chang, Daniel B. Neill, Emily Black
arXiv:2606. 10911v1 Announce Type: cross Abstract: Claims about the robustness and fairness of deepfake speech detectors are only as credible as the datasets used to train and evaluate those systems.
By Vojt\v{e}ch Stan\v{e}k, Eva Trnovsk\'a, Kamil Malinka, Anton Firc
arXiv:2602.01161v2 Announce Type: replace
Abstract: The global deployment of large language models (LLMs) has raised concerns about cultural misalignment, yet the linguistic properties of fine-tuning...
By Reem I. Masoud, Chen Feng, Shunta Asano, Saied Alshahrani, Philip Colin Treleaven, Miguel R. D. Rodrigues
MBBQ (Multilingual Bias Benchmark for Question-answering) is a curated extension of the English BBQ dataset that includes Dutch, Spanish, and Turkish, designed to measure stereotypes across these languages while controlling for cultural differences and task accuracy. The dataset also provides a parallel control set to assess question-answering performance independently of bias. Experiments with several open-source and proprietary LLMs show that non‑English languages can exhibit greater bias than English, and cross‑lingual bias differences persist except in the most accurate models.
By Vera Neplenbroek, Arianna Bisazza, Raquel Fern\'andez
The paper demonstrates that pre‑trained language models can perform record linkage and demographic inference from names without task‑specific training. EnsembleLink, an algorithm that combines pre‑trained embeddings with string‑based scorers, achieves performance comparable to or better than prior methods across diverse scripts and formats. Large language models reach up to 83.8% accuracy on U.S. voter files for ethnicity inference, surpassing name‑supplemented BISG and enabling inference for non‑U.S. ethnic categories while reducing bias in geographic racial gap estimates.
By Noah Dasanaike
The paper introduces Population Fidelity, an evaluation framework for assessing how well large language models (LLMs) represent human population attitudes. It focuses on three dimensions: group-level accuracy, between-group variation, and the structure of that variation. Using the framework, the authors replicate a prior study on machine bias and test cultural fine-tuning, finding that while fine-tuning improves overall alignment, it does not enhance representation of within-population differences.
By Neemias B. da Silva, Martin Lukk, Ali Sutani, Abhishek Moturu, Harris Yang, Daniel Silver, Matt Ratto, Thiago H. Silva
The paper evaluates three open‑weight large language models—Gemma3‑12B (USA), Bielik‑11B‑v3 (Poland), and Qwen3‑4B (China)—against World Values Survey data for 63 demographic personas across three countries, using normalized Wasserstein distance to measure cultural misalignment. Surprisingly, none of the models shows a preference for its home country; Qwen3‑4B, built in China, has the highest misalignment for Chinese respondents. Targeted LoRA fine‑tuning on the five worst‑case personas, with fewer than 1,200 training pairs and under 15 minutes on a single GPU, reduces bias by 16.8% for Bielik‑11B, but the fine‑tuning redistributes bias rather than eliminating it, shifting worst‑case personas from American to Chinese elderly.
By Antoni Czolgowski, Abel Iyasele
The paper shows that large language models can infer ethnicity from names with higher accuracy than Bayesian Improved Surname Geocoding (BISG), achieving up to 84.7% accuracy on balanced samples from Florida and North Carolina voter files. Six models, including Gemini 3 Flash, GPT‑4o, and open‑source alternatives, were tested, with extended reasoning and metadata such as party registration further improving performance. The method also reduces income bias present in BISG and is validated across diverse populations in India, Uganda, Nepal, Armenia, Chile, and Costa Rica, with fine‑tuned small transformer models surpassing BISG while allowing local deployment at no cost.
By Noah Dasanaike
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
arXiv:2608. 13624v1 Announce Type: cross Abstract: Large Audio Language Models (LALMs) have seen increasing use for audio understanding tasks such as speech recognition and audio question answering, raising concerns about fairness across demographic subgroups.
By Zhe Liu
arXiv:2610.22545v1 Announce Type: new
Abstract: Stereotypes are subjective constructs that are mediated by the cultural context in which they are viewed. Successfully modeling them thus also requires...
By Maximilian Splieth\"over, Jannes L\"otfering, Dominik Bachmann, Alexander Espig, Willem Zuidema, Katrin Schulz, Henning Wachsmuth
arXiv:2501. 02211v2 Announce Type: replace-cross Abstract: Large language models (LLMs) reproduce homogeneity bias -- the tendency to portray marginalized groups as more internally similar than dominant groups -- but whether this bias is stable or an artifact of inference settings has only been studied in single proprietary models.
By Messi H. J. Lee