Format as a Prior: Quantifying and Analyzing Bias in LLMs for Heterogeneous Data

Jiacheng Liu; Mayi Xu; Qiankun Pi; Wenli Li; Ming Zhong; Yuanyuan Zhu; Mengchi Liu; Tieyun Qian

AAAI 2026technical0 citations

Format as a Prior: Quantifying and Analyzing Bias in LLMs for Heterogeneous Data

Jiacheng Liu, Mayi Xu, Qiankun Pi, Wenli Li, Ming Zhong, Yuanyuan Zhu, Mengchi Liu, Tieyun Qian

Abstract

Large Language Models (LLMs) are increasingly employed in applications that require processing information from heterogeneous formats, including texts, tables, infoboxes, and knowledge graphs. However, systematic biases toward particular formats may undermine LLMs

BibTeX

@inproceedings{aaai2026_formatasapriorqu,
  title = {Format as a Prior: Quantifying and Analyzing Bias in LLMs for Heterogeneous Data},
  author = {Jiacheng Liu and Mayi Xu and Qiankun Pi and Wenli Li and Ming Zhong and Yuanyuan Zhu and Mengchi Liu and Tieyun Qian},
  booktitle = {AAAI 2026},
  year = {2026}
}