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.

This paper describes our system used in the SemEval-2023 Task12: Sentiment Analysis for Low-resource African Languages using Twit- ter Dataset (Muhammad et al., 2023c). The AfriSenti-SemEval Shared Task 12 is based on a collection of Twitter datasets in 14 African languages for sentiment classification. It con- sists of three sub-tasks. Task A is a monolin- gual sentiment classification which covered 12 African languages. Task B is a multilingual sen- timent classification which combined training data from Task A (12 African languages). Task C is a zero-shot sentiment classification. We uti- lized various strategies, including monolingual training, multilingual mixed training, and trans- lation technology, and proposed a weighted vot- ing method that combined the results of differ- ent strategies. Substantially, in the monolingual subtask, our system achieved Top-1 in two lan- guages (Yoruba and Twi) and Top-2 in four languages (Nigerian Pidgin, Algerian Arabic, and Swahili, Multilingual). In the multilingual subtask, Our system achived Top-2 in publish leaderBoard.