Attribution Bias in Large Language Models
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 acro
Record details
Published: 6 April 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Finance, VC & PE
Retrieved: 14 July 2026
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ethics.ai (6 April 2026), “Attribution Bias in Large Language Models,” evidence record 6292, https://ethics.ai/record/6292 (originally published by arXiv).
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