EtiCor++: Towards Understanding Etiquettical Bias in LLMs
Ashutosh Dwivedi, Siddhant Shivdutt Singh, Ashutosh Modi
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.
BibTeX
@inproceedings{dwivedi-etal-2025-eticor,
title = "{E}ti{C}or++: Towards Understanding Etiquettical Bias in {LLM}s",
author = "Dwivedi, Ashutosh and
Singh, Siddhant Shivdutt and
Modi, Ashutosh",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.488/",
doi = "10.18653/v1/2025.findings-acl.488",
pages = "9355--9376",
ISBN = "979-8-89176-256-5"
}