This system paper describes our participation in the SemEval-2025 Task-7 ``Everyday Knowledge Across Diverse Languages and Cultures''. We attended two subtasks, i.e., Track 1: Short Answer Questions (SAQ), and Track 2: Multiple-Choice Questions (MCQ). The methods we used are retrieval augmented generation (RAGs) with open-sourced smaller LLMs (OS-sLLMs). To better adapt to this shared task, we created our own culturally aware knowledge base (CulKBs) by extracting Wikipedia content using keyword lists we prepared. We extracted both culturally-aware wiki-text and country-specific wiki-summary. In addition to the local CulKBs, we also have one system integrating live online search output via DuckDuckGo. Towards better privacy and sustainability, we aimed to deploy smaller LLMs (sLLMs) that are open-sourced on the Ollama platform. We share the prompts we developed using refinement techniques and report the learning curve of such prompts. The tested languages are English, Spanish, and Chinese for both tracks. Our resources and codes are shared via this https URL
FLANS在SemEval-2026任务7中的实践：利用开源小型大语言模型与检索增强生成技术处理多语言与跨文化的日常知识问答 / FLANS at SemEval-2026 Task 7: RAG with Open-Sourced Smaller LLMs for Everyday Knowledge Across Diverse Languages and Cultures
这篇论文介绍了团队在SemEval-2025跨文化日常知识问答任务中，通过构建一个包含文化感知知识的本地知识库，并融合在线搜索，利用开源小型大语言模型结合检索增强生成技术，在英语、西班牙语和中文的简答与选择题上进行了有效尝试，旨在平衡性能、隐私与可持续性。
源自 arXiv: 2603.01910
