arXiv Computation and Language
Using Embedding Models to Improve Probabilistic Race Prediction
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
Related stories
arXiv Computation and Language
Oct 15
Pre-Trained Language Models as Zero-Shot Tools for Social Science Research
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.
arXiv Computation and Language
Oct 16
Large Language Models Naively Recover Ethnicity from Individual Records
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.
arXiv Machine Learning
Jul 21
STRATA: A Name-and-Geography Race Inference Model for Fair Lending and Housing Equity Applications
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.
arXiv Computation and Language
Oct 16
Name-Bias in Large Language Models
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.
Hugging Face Trending Papers
Oct 15
Name-Bias in Large Language Models
Names carry demographic indicators such as age, gender, and race, which makes them a subtle but strong target of bias in large language models (LLMs). In this work, we present an evaluation of name-ba...
arXiv Machine Learning
Sep 2
SAGE: Subpopulation-Aware Generative Enhancement for Mitigating Spurious Correlations
SAGE (Subpopulation-Aware Generative Enhancement) is a two-stage generative augmentation framework designed to mitigate spurious correlations in machine learning when group labels are unavailable. It uses cluster-derived sub-labels and class labels to fine‑tune a conditional generative model and text encoder, producing synthetic data that fills underrepresented regions and creates a balanced validation set for last‑layer reweighting. Experiments show SAGE improves worst‑group accuracy to 89.5%, 85.7%, and 79.1% on Waterbirds, CelebA, and MetaShift, outperforming existing group‑label‑free baselines by up to 7.7 percentage points.