@inproceedings{xu-etal-2026-cultural,
title = "Cultural and Knowledge Biases in {LLM}s through the Lens of Entity-Aware Machine Translation",
author = "Xu, Lu and
Moroni, Luca and
Navigli, Roberto",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.692/",
doi = "10.63317/3jxgnspt4srr",
pages = "8794--8812",
abstract = "Large Language Models (LLMs) demonstrate strong multilingual capabilities yet exhibit systematic cultural biases that affect entity-aware machine translation. While external knowledge integration improves translation accuracy, the extent of these benefits across varying degrees of cultural specificity remains unexplored. We propose a three-level cultural specificity framework: Culturally Agnostic, Culturally Sensitive, and Culturally Local, to systematically analyze how cultural context affects entity translation difficulty and the utility of external knowledge. Through experiments spanning 11 LLMs and 10 languages, we demonstrate that external knowledge provides substantially greater improvements for culturally local entities (up to 70{\%} in m-ETA) compared to culturally agnostic ones. Our analysis reveals distinct behavioral patterns across model tiers: closed and open-weight models show synergistic improvements in both entity accuracy and overall translation quality, while open-data models struggle with instruction-following despite improved entity accuracy."
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<abstract>Large Language Models (LLMs) demonstrate strong multilingual capabilities yet exhibit systematic cultural biases that affect entity-aware machine translation. While external knowledge integration improves translation accuracy, the extent of these benefits across varying degrees of cultural specificity remains unexplored. We propose a three-level cultural specificity framework: Culturally Agnostic, Culturally Sensitive, and Culturally Local, to systematically analyze how cultural context affects entity translation difficulty and the utility of external knowledge. Through experiments spanning 11 LLMs and 10 languages, we demonstrate that external knowledge provides substantially greater improvements for culturally local entities (up to 70% in m-ETA) compared to culturally agnostic ones. Our analysis reveals distinct behavioral patterns across model tiers: closed and open-weight models show synergistic improvements in both entity accuracy and overall translation quality, while open-data models struggle with instruction-following despite improved entity accuracy.</abstract>
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%0 Conference Proceedings
%T Cultural and Knowledge Biases in LLMs through the Lens of Entity-Aware Machine Translation
%A Xu, Lu
%A Moroni, Luca
%A Navigli, Roberto
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F xu-etal-2026-cultural
%X Large Language Models (LLMs) demonstrate strong multilingual capabilities yet exhibit systematic cultural biases that affect entity-aware machine translation. While external knowledge integration improves translation accuracy, the extent of these benefits across varying degrees of cultural specificity remains unexplored. We propose a three-level cultural specificity framework: Culturally Agnostic, Culturally Sensitive, and Culturally Local, to systematically analyze how cultural context affects entity translation difficulty and the utility of external knowledge. Through experiments spanning 11 LLMs and 10 languages, we demonstrate that external knowledge provides substantially greater improvements for culturally local entities (up to 70% in m-ETA) compared to culturally agnostic ones. Our analysis reveals distinct behavioral patterns across model tiers: closed and open-weight models show synergistic improvements in both entity accuracy and overall translation quality, while open-data models struggle with instruction-following despite improved entity accuracy.
%R 10.63317/3jxgnspt4srr
%U https://aclanthology.org/2026.lrec-1.692/
%U https://doi.org/10.63317/3jxgnspt4srr
%P 8794-8812
Markdown (Informal)
[Cultural and Knowledge Biases in LLMs through the Lens of Entity-Aware Machine Translation](https://aclanthology.org/2026.lrec-1.692/) (Xu et al., LREC 2026)
ACL