用AI生成文档特征提升问答系统检索效果
Enhancing Retrieval in QA Systems with Derived Feature Association
- 在文档入库时用大模型生成摘要、问题等衍生特征
- 在长文本问答任务中显著提升RAG系统性能
- 适合需要精准检索的复杂问答场景
检索增强生成（RAG）已成为长上下文问答系统的标准方案。然而，典型的RAG实现依赖于简单的检索机制：仅选择与查询嵌入最相似的文本作为相关文档。这在主观性问答任务中存在缺陷，因为最相关的文本可能并不直接包含答案。本文提出一种RAG的新扩展方法——从AI生成文档中检索（RAIDD），通过在文档摄入阶段利用大模型推导出摘要、示例问题等推断特征，从而增强检索能力。实验证明，该方法显著提升了RAG系统在长上下文问答任务中的表现。
原文摘要 · Abstract (English)
Retrieval augmented generation (RAG) has become the standard in long context question answering (QA) systems. However, typical implementations of RAG rely on a rather naive retrieval mechanism, in which texts whose embeddings are most similar to that of the query are deemed most relevant. This has consequences in subjective QA tasks, where the most relevant text may not directly contain the answer. In this work, we propose a novel extension to RAG systems, which we call Retrieval from AI Derived Documents (RAIDD). RAIDD leverages the full power of the LLM in the retrieval process by deriving inferred features, such as summaries and example questions, from the documents at ingest. We demonstrate that this approach significantly improves the performance of RAG systems on long-context QA tasks.
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