@inproceedings{ning-2026-locuprompt,
title = "{L}ocu{P}rompt at {S}em{E}val-2026 Task 7: A Multilingual Prompting Framework for Cross-Cultural Everyday Knowledge in {LLM}s",
author = "Ning, Ningjingke",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.134/",
pages = "973--980",
ISBN = "979-8-89176-414-9",
abstract = "Understanding everyday cultural knowledge remains a fundamental challenge for large language models (LLMs). This paper presents LocuPrompt, a multilingual framework for SemEval-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures. To address Short Answer Questions (SAQ), we employ an English-pivot generation strategy with back-translation, combined with empirical locale-specific routing that dynamically assigns the optimal LLM to each target region. For Multiple-Choice Questions (MCQ), we apply parameter-efficient fine-tuning to a robust multilingual base model, utilizing locale-aware instructions that frame the LLM as a ``local resident.'' By integrating strategic model selection with resource-efficient adaptation, LocuPrompt effectively bridges cross-lingual cultural gaps while maintaining a fully reproducible pipeline."
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%0 Conference Proceedings
%T LocuPrompt at SemEval-2026 Task 7: A Multilingual Prompting Framework for Cross-Cultural Everyday Knowledge in LLMs
%A Ning, Ningjingke
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F ning-2026-locuprompt
%X Understanding everyday cultural knowledge remains a fundamental challenge for large language models (LLMs). This paper presents LocuPrompt, a multilingual framework for SemEval-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures. To address Short Answer Questions (SAQ), we employ an English-pivot generation strategy with back-translation, combined with empirical locale-specific routing that dynamically assigns the optimal LLM to each target region. For Multiple-Choice Questions (MCQ), we apply parameter-efficient fine-tuning to a robust multilingual base model, utilizing locale-aware instructions that frame the LLM as a “local resident.” By integrating strategic model selection with resource-efficient adaptation, LocuPrompt effectively bridges cross-lingual cultural gaps while maintaining a fully reproducible pipeline.
%U https://aclanthology.org/2026.semeval-1.134/
%P 973-980
Markdown (Informal)
[LocuPrompt at SemEval-2026 Task 7: A Multilingual Prompting Framework for Cross-Cultural Everyday Knowledge in LLMs](https://aclanthology.org/2026.semeval-1.134/) (Ning, SemEval 2026)
ACL