Token-Level Entropy Reveals Demographic Disparities in Large Language Models

תקציר מקורי באנגליתarXiv:2501.19337v5 Announce Type: replace Abstract: A name alone measurably reshapes a language model's next-token distribution before a single token is sampled. We measure full-vocabulary Shannon entropy of the next-token distribution across six open-weight model families on 5,760 sentence-completion prompts in which race and gender are signaled only by a first name. Black-associated names co-occur with higher first-token entropy and more diverse continuations than White-associated names -- directionally consistent in all six instruction-tuned models under shared raw-text input, all six base checkpoints, and, for output diversity, five of six models under native chat formatting -- opposite to the homogeneity bias documented under explicit group labels (Lee et al., 2024). The gap persists