研究發現:RAG系統中文檔數量影響AI語言模型性能
耶路撒冷希伯來大學的研究人員最近發現，在檢索增強生成（RAG）系統中，即使總文本長度保持不變，處理的文檔數量也會顯著影響語言模型的性能。研究團隊利用MuSiQue驗證數據集中的2，417個問題進行實驗，每個問題鏈接到20個維基百科段落。其中兩到四段包含相關答案信息，其餘段落作爲干擾項。爲研究文檔數量的影響，團隊創建了多個數據分區，逐步將文檔數量從20個減少到最少只保留包含相關信息的2-4個文檔。爲確保總標記數一致，研究人員使用原始維基百科文章的文本擴展了保留
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