@inproceedings{jin-etal-2026-king001,
title = "king001 at {S}em{E}val-2026 Task 7: Cross-Language Cultural Everyday Knowledge {Q} A System Based on {RAG}",
author = "Jin, Meizhi and
Meng, Zhichao and
Yin, Junqi and
Jiang, Lianxin and
Li, Jianyu",
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-1.142/",
pages = "1032--1049",
ISBN = "979-8-89176-414-9",
abstract = "This paper describes our system used in the SemEval-2026 Task 7: Cross-Language Cultural Everyday Knowledge QA (track 1). Cultural knowledge typically exhibits significant regional specificity and is deeply rooted in particular linguistic conventions, posing severe challenges to general-purpose large language models (LLMs). We propose a retrieval-augmented generation (RAG) framework: this framework utilizes text-embedding-v4 as the retrieval core to precisely extract social knowledge and expression patterns from region-specific large-scale multilingual cultural knowledge bases, and drives the gpt-5.2-chat model to generate concise answers that are both logically factual and highly aligned with the target region{'}s cultural context. In the official evaluation, our system ranked first among all participating teams with a total score of 78.7672, fully demonstrating the method{'}s outstanding performance in cross-cultural accuracy and linguistic authenticity."
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<abstract>This paper describes our system used in the SemEval-2026 Task 7: Cross-Language Cultural Everyday Knowledge QA (track 1). Cultural knowledge typically exhibits significant regional specificity and is deeply rooted in particular linguistic conventions, posing severe challenges to general-purpose large language models (LLMs). We propose a retrieval-augmented generation (RAG) framework: this framework utilizes text-embedding-v4 as the retrieval core to precisely extract social knowledge and expression patterns from region-specific large-scale multilingual cultural knowledge bases, and drives the gpt-5.2-chat model to generate concise answers that are both logically factual and highly aligned with the target region’s cultural context. In the official evaluation, our system ranked first among all participating teams with a total score of 78.7672, fully demonstrating the method’s outstanding performance in cross-cultural accuracy and linguistic authenticity.</abstract>
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%0 Conference Proceedings
%T king001 at SemEval-2026 Task 7: Cross-Language Cultural Everyday Knowledge Q A System Based on RAG
%A Jin, Meizhi
%A Meng, Zhichao
%A Yin, Junqi
%A Jiang, Lianxin
%A Li, Jianyu
%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 jin-etal-2026-king001
%X This paper describes our system used in the SemEval-2026 Task 7: Cross-Language Cultural Everyday Knowledge QA (track 1). Cultural knowledge typically exhibits significant regional specificity and is deeply rooted in particular linguistic conventions, posing severe challenges to general-purpose large language models (LLMs). We propose a retrieval-augmented generation (RAG) framework: this framework utilizes text-embedding-v4 as the retrieval core to precisely extract social knowledge and expression patterns from region-specific large-scale multilingual cultural knowledge bases, and drives the gpt-5.2-chat model to generate concise answers that are both logically factual and highly aligned with the target region’s cultural context. In the official evaluation, our system ranked first among all participating teams with a total score of 78.7672, fully demonstrating the method’s outstanding performance in cross-cultural accuracy and linguistic authenticity.
%U https://aclanthology.org/2026.semeval-1.142/
%P 1032-1049
Markdown (Informal)
[king001 at SemEval-2026 Task 7: Cross-Language Cultural Everyday Knowledge Q A System Based on RAG](https://aclanthology.org/2026.semeval-1.142/) (Jin et al., SemEval 2026)
ACL