研究发现:RAG系统中文档数量影响AI语言模型性能
耶路撒冷希伯来大学的研究人员最近发现，在检索增强生成（RAG）系统中，即使总文本长度保持不变，处理的文档数量也会显著影响语言模型的性能。研究团队利用MuSiQue验证数据集中的2，417个问题进行实验，每个问题链接到20个维基百科段落。其中两到四段包含相关答案信息，其余段落作为干扰项。为研究文档数量的影响，团队创建了多个数据分区，逐步将文档数量从20个减少到最少只保留包含相关信息的2-4个文档。为确保总标记数一致，研究人员使用原始维基百科文章的文本扩展了保留
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