构建瑞典专属事实知识测试集，评估模型对本地人物事件的掌握能力
A Diagnostic Benchmark for Sweden-Related Factual Knowledge
- 人工构建聚焦瑞典人物与事件的问答数据集
- 小模型若覆盖瑞典内容，表现可媲美三倍大的多语言模型
- 瑞典语持续预训练提升本地知识但导致部分遗忘
许多瑞典基准测试是美国中心基准的翻译，无法有效检验与瑞典相关的特定知识。为此，我们构建了一个手工编写、专用于瑞典人物与事件的问答基准，内容灵感来自一档知名广播节目及瑞典重大体育赛事，涵盖国际媒体覆盖极少的主题。该数据集可用于评估不同规模与瑞典覆盖程度模型的事实记忆能力，并支持跨语言事实一致性探测（含英文翻译）。实验发现，具备较强瑞典知识的小模型在回忆瑞典相关事实时，表现可媲美三倍大小的多语言模型；持续用瑞典语预训练虽能提升本地知识，但会导致部分原有知识遗忘。结果表明该数据集在研究多语言模型语言适配与知识保留方面具有诊断潜力。
原文摘要 · Abstract (English)
Many Swedish benchmarks are translations of US-centric benchmarks and are therefore not suitable for testing knowledge that is particularly relevant, or even specific, to Sweden. We therefore introduce a manually written question-answering benchmark specifically targeted at Sweden-related personalities and events, many of which receive very limited coverage in international media. Our annotators drew inspiration from a popular radio program featuring public figures from culture and media, as well as major sports events in Sweden. The dataset can be used to measure factual recall across models of varying sizes and degrees of Swedish coverage, and allows probing of cross-lingual factual consistency, as it contains English translations. Using the dataset, we find that smaller models with stronger Swedish coverage perform comparably to a multilingual model three times larger in recalling Sweden-related facts. We also observe that continued pre-training on Swedish generally improves factual knowledge but leads to partial forgetting of previously known information. These results demonstrate the dataset's potential as a diagnostic tool for studying language adaptation and knowledge retention in multilingual models during language adaptation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。