Quantifying Fairness in LLMs Beyond Tokens: A Semantic and Statistical Perspective

Описание: Authors: Weijie Xu, Yiwen Wang, Chi Xue, Xiangkun Hu, Xi Fang, Guimin Dong, Chandan K. Reddy

Large Language Models (LLMs) often generate responses with inherent biases,

undermining their reliability in real-world applications. Existing evaluation meth-

ods often overlook biases in long-form responses and the intrinsic variability of

LLM outputs. To address these challenges, we propose FiSCo (Fine-grained Se-

mantic Comparison), a novel statistical framework to evaluate group-level fairness

in LLMs by detecting subtle semantic differences in long-form responses across

demographic groups. Unlike prior work focusing on sentiment or token-level

comparisons, FiSCo goes beyond surface-level analysis by operating at the claim

level, leveraging entailment checks to assess the consistency of meaning across

responses. We decompose model outputs into semantically distinct claims and

apply statistical hypothesis testing to compare inter- and intra-group similarities,

enabling robust detection of subtle biases. We formalize a new group counterfac-

tual fairness definition and validate FiSCo on both synthetic and human-annotated

datasets spanning gender, race, and age. Experiments show that FiSCo more

reliably identifies nuanced biases while reducing the impact of stochastic LLM

variability, outperforming various evaluation metrics.