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
EnsembleLink is a record‑linkage method that achieves high accuracy without requiring any training labels. It uses pre‑trained language models to capture semantic relationships, enabling it to match records across diverse domains such as city names, person names, organizations, multilingual political parties, and bibliographic entries. The approach runs locally on open‑source models, avoiding external API calls, and completes typical linkage tasks in minutes.
By Noah Dasanaike
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
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. 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
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