Large language models unevenly recognize names by race and gender
Who Gets a Token, and What Does It Carry? Unequal Name Support and Concept Access in Large Language Models
Computation and LanguageArtificial IntelligenceComputers and SocietyEmerging TechnologiesMachine Learning
Summary
Different names aren't always treated equally by large language models because some names are recognized as single tokens, while others are broken into smaller parts. The authors show this unequal tokenization happens more often depending on the name's race or gender associations. They created a method called NameTrace to measure how this affects models’ understanding of concepts linked to those names. Results indicate that these differences matter for tasks like hiring or lending decisions made by the models.
What this means in practice
- •For nlp engineers: Diagnose and adjust tokenization effects on name processing to improve fairness across race and gender groups in language models.
- •For bias mitigation teams: Use NameTrace to quantify and address unequal name representations that impact model decisions in hiring, lending, and clinical assessment systems.
Authors
Mir Tafseer Nayeem, Davood Rafiei
Abstract
Names are personal identifiers, but they also carry social meaning and are widely used to evaluate how language models treat different people. Such evaluations typically assume that matched names are comparable model inputs. We show that this assumption often fails at the lexical interface: matched names are not necessarily matched inputs. Some names receive direct single-token access, while others are assembled from multiple subwords, creating unequal name-surface support. Across nearly half a million first names and 12 LLM-associated tokenizers, direct lexical access is highly selective, model dependent, and uneven across race- and gender-associated name metadata. We introduce NameTrace, a model-native, fine-grained, pre-behavioral framework for measuring whether unequal name-surface support remains a vocabulary property or becomes visible in task-relevant internal representations. NameTrace measures concept accessibility from the model's own probabilities over task-specific adjective axes with continuous task-aligned weights. On matched atomic and short-fragmented names within the same race/ethnicity--gender-associated strata, support predicts systematic differences in concept accessibility across fellowship, hiring, clinical assessment, and lending. These differences persist across all eight matched strata, extend across model families, and transfer to unseen names. Hidden-state interventions further show that the measured task directions have downstream leverage, shifting later constrained choices. Unequal lexical support is therefore demographically structured at the input and remains visible in task-relevant model computation. NameTrace makes lexical comparability measurable, supporting a broader principle: behavioral comparability begins with lexical comparability.