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