EtiCor++: Towards Understanding Etiquettical Bias in LLMs
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915335287865344 |
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| author | Dwivedi, Ashutosh Singh, Siddhant Shivdutt Modi, Ashutosh |
| author_facet | Dwivedi, Ashutosh Singh, Siddhant Shivdutt Modi, Ashutosh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08488 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EtiCor++: Towards Understanding Etiquettical Bias in LLMs Dwivedi, Ashutosh Singh, Siddhant Shivdutt Modi, Ashutosh Computation and Language Artificial Intelligence Computers and Society 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. |
| title | EtiCor++: Towards Understanding Etiquettical Bias in LLMs |
| topic | Computation and Language Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2506.08488 |