This paper introduces an approach to question answering over knowledge bases like Wikipedia and Wikidata by performing "question-to-question" matching and retrieval from a dense vector embedding store. Instead of embedding document content, we generate a comprehensive set of questions for each logical content unit using an instruction-tuned LLM. These questions are vector-embedded and stored, mapping to the corresponding content. Vector embedding of user queries are then matched against this question vector store. The highest similarity score leads to direct retrieval of the associated article content, eliminating the need for answer generation. Our method achieves high cosine similarity ( > 0.9 ) for relevant question pairs, enabling highly precise retrieval. This approach offers several advantages including computational efficiency, rapid response times, and increased scalability. We demonstrate its effectiveness on Wikipedia and Wikidata, including multimedia content through structured fact retrieval from Wikidata, opening up new pathways for multimodal question answering.

翻译：本文提出一种基于知识库（如维基百科和维基数据）的问答方法，该方法通过从稠密向量嵌入存储中执行“问题到问题”的匹配与检索来实现。与嵌入文档内容不同，我们使用指令微调的大语言模型为每个逻辑内容单元生成一组全面的问题。这些问题经向量嵌入后存储，并映射至相应内容。用户查询经向量嵌入后，与此问题向量库进行匹配。通过最高相似度得分直接检索关联的文章内容，从而无需生成答案。我们的方法在相关问题上对间实现了较高的余弦相似度（> 0.9），支持高精度检索。该方法具有计算效率高、响应速度快、可扩展性强等优势。我们在维基百科和维基数据上验证了其有效性，包括通过维基数据的结构化事实检索处理多媒体内容，为多模态问答开辟了新途径。