English

EtiCor++: Towards Understanding Etiquettical Bias in LLMs

Computation and Language 2025-06-11 v1 Artificial Intelligence Computers and Society

Abstract

In recent years, researchers have started analyzing the cultural sensitivity of LLMs. In this respect, Etiquettes have been an active area of research. Etiquettes are region-specific and are an essential part of the culture of a region; hence, it is imperative to make LLMs sensitive to etiquettes. However, there needs to be more resources in evaluating LLMs for their understanding and bias with regard to etiquettes. In this resource paper, we introduce EtiCor++, a corpus of etiquettes worldwide. We introduce different tasks for evaluating LLMs for knowledge about etiquettes across various regions. Further, we introduce various metrics for measuring bias in LLMs. Extensive experimentation with LLMs shows inherent bias towards certain regions.

Keywords

Cite

@article{arxiv.2506.08488,
  title  = {EtiCor++: Towards Understanding Etiquettical Bias in LLMs},
  author = {Ashutosh Dwivedi and Siddhant Shivdutt Singh and Ashutosh Modi},
  journal= {arXiv preprint arXiv:2506.08488},
  year   = {2025}
}

Comments

Accepted at ACL Findings 2025, 22 pages (9 pages main content + 4 pages references + 9 pages appendix)