Attribution Bias in Large Language Models
This addresses representational fairness in LLMs for users relying on them for accurate information retrieval, though it is incremental as it builds on existing bias evaluation methods.
The authors tackled the problem of demographic bias in quote attribution by Large Language Models (LLMs), introducing AttriBench, a balanced benchmark dataset, and found large disparities in accuracy and suppression across race, gender, and intersectional groups, with suppression being widespread and unevenly distributed.
As Large Language Models (LLMs) are increasingly used to support search and information retrieval, it is critical that they accurately attribute content to its original authors. In this work, we introduce AttriBench, the first fame- and demographically-balanced quote attribution benchmark dataset. Through explicitly balancing author fame and demographics, AttriBench enables controlled investigation of demographic bias in quote attribution. Using this dataset, we evaluate 11 widely used LLMs across different prompt settings and find that quote attribution remains a challenging task even for frontier models. We observe large and systematic disparities in attribution accuracy between race, gender, and intersectional groups. We further introduce and investigate suppression, a distinct failure mode in which models omit attribution entirely, even when the model has access to authorship information. We find that suppression is widespread and unevenly distributed across demographic groups, revealing systematic biases not captured by standard accuracy metrics. Our results position quote attribution as a benchmark for representational fairness in LLMs.