Simorgh at SemEval-2026 task 7: 面向低资源文化推理的多语言问答中的区域感知混合检索
Simorgh at SemEval-2026 task 7: Region-Aware Hybrid Retrieval for Low-Resource Cultural Reasoning in Multilingual Question Answering
AI总结：
提出区域感知混合检索方法，结合BM25和稠密语义相似度与区域加权启发式，以提升多语言文化问答的跨语言稳定性。
摘要：
尽管大型语言模型（LLMs）在通用领域的推理任务中表现出色，但在数字和文本数据有限的语种中，面对文化相关知识时可能遇到挑战。本文利用BLEnD基准研究文化相关的多项选择问答，该基准包含30种语言的多语料库，涵盖饮食、体育、家庭等社会文化领域。我们提出一种区域感知混合检索方法，结合BM25词汇匹配和稠密语义相似度与区域加权启发式，以提高答案的相关性。检索到的文档用于构建结构化提示，输入Qwen3-14B量化模型，并采用基于logit的确定性答案选择。实验结果表明，与纯参数推理相比，混合检索方法在文化问答中提升了跨语言稳定性。然而，训练数据量不同的语言之间仍存在显著性能差距，这表明检索增强方法并未完全克服训练数据不平衡问题。
英文摘要：
Although Large Language Models (LLMs) demonstrate excellent capabilities and performance for general reasoning tasks within the general public domain, they may face challenges with culturally grounded knowledge within languages with limited digital and textual data. In this paper, we investigate culturally grounded multiple-choice question answering with the BLEnD benchmark, which consists of a multilingual corpus of 30 languages and covers various socio-cultural domains, such as cuisine, sports, family, etc. We propose a region-aware hybrid retrieval approach that combines BM25 lexical matching and dense semantic similarity with regional weighting heuristics to improve the relevance of the answer. The retrieved documents are used to construct a structured prompt for the Qwen3-14B quantized model with logit-based deterministic answer selection. The experimental results show improvements to cross-lingual stability with the hybrid retrieval approach over pure parametric inference for culturally grounded question answering. However, there are still notable performance gaps between languages with more and less training data. This shows that the limitations of the retrieval augmentation approach are not entirely overcome by the training data imbalance problem.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
