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
arXiv:2504. 21259v2 Announce Type: replace-cross Abstract: Accurate imputation of race and ethnicity (R&E) is essential for fair lending compliance under ECOA, HMDA, and the Community Reinvestment Act, where up to 15% of mortgage applications carry missing race data and regulated institutions bear responsibility for identifying disparities on those records.
By S. Chalavadi, A. Pastor, T. Leitch
The study investigates cultural biases in large language models (LLMs) by testing their ability to perform author profiling—inferring singers’ gender and ethnicity—from song lyrics in a zero‑shot setting. Evaluating over 10,000 lyrics across several open‑source models, the authors find that most LLMs default toward North American ethnicity, while DeepSeek‑1.5B leans toward Asian ethnicity, and that Ministral‑8B exhibits the strongest ethnicity bias whereas Gemma‑12B is the most balanced. The paper introduces two fairness metrics, Modality Accuracy Divergence (MAD) and Recall Divergence (RD), to quantify these disparities and provides code and results publicly on GitHub and HuggingFace.
By Valentin Lafargue, Ariel Guerra-Adames, Emmanuelle Claeys, Elouan Vuichard, Jean-Michel Loubes
arXiv:2501. 02211v3 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 generalizes across models, is stable under different inference settings, or depends on how group identity is signaled remains unstudied.
By Messi H. J. Lee
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
The study evaluates name-based bias in six local large language models using a fill-in-the-blank task with first names from U.S. Social Security and a race classification dataset. Across 300,000 responses, the models consistently favored names that were less stereotypically male, older, popular, and white, with gender bias being the strongest effect. The bias varied with sentence context but remained significant, underscoring the persistence of name bias in local LLMs.
By Kalyan Bhetwal, Ceren Mert, Ian Whitehouse
arXiv:2603. 13891v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for automated text annotation in tasks ranging from academic research to content moderation and hiring.
By Petter T\"ornberg
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 investigates how large language models (LLMs) represent nine transborder ethnic communities along China’s southwestern border with mainland Southeast Asia, such as the Sami, Rohingya, and Kurds. It introduces the concept of identity ossification, where LLMs reduce fluid, cross-border identities to a single nation-state narrative, and presents the Transborder Identity Ossification Diagnostic Framework (TIODF) to differentiate between knowledge gaps and narrative framing. Experiments with GPT‑5.1 and DeepSeek‑V3.2 in English and Chinese show that ossification rates vary widely, with DeepSeek‑V3.2’s Chinese responses exhibiting the phenomenon in 62.6% of cases, indicating that narrative framing rather than mere knowledge absence drives the issue.
By Minglu Sun, Lan Chen, Hang Jiang
The paper presents multicalibration as a method for unbiased prevalence estimation when measurement devices (diagnostic tests, classifiers, or large language models) exhibit covariate shift. It shows that standard calibration fails under such shift, while multicalibration—calibrating conditional on input features—maintains near-zero bias in simulations and real-world applications. The authors demonstrate the approach on employment data across U.S. states and political text classification across four countries, emphasizing the need for calibration data that covers key feature dimensions.
By Fridolin Linder, Thomas Leeper, Daniel Haimovich, Niek Tax, Lorenzo Perini, Milan Vojnovic
arXiv:2604. 01925v2 Announce Type: replace-cross Abstract: Large Language Models increasingly suppress biased outputs when demographic identity is stated explicitly, yet may still exhibit implicit biases when identity is conveyed indirectly.
By Bhaskara Hanuma Vedula, Darshan Anghan, Ishita Goyal, Ponnurangam Kumaraguru, Abhijnan Chakraborty
arXiv:2607. 26348v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as synthetic users, stand-ins for human respondents whose simulated answers feed product, policy, and market decisions.
By Zihan Chen, Di Zhu, Lei Nico Zheng