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.

翻译：检索增强生成（RAG）已成为长上下文问答（QA）系统的标准范式。然而，典型的RAG实现依赖于一种较为朴素的检索机制，即仅将嵌入表示与查询最相似的文本视为最相关的内容。这在主观性QA任务中会带来问题，因为最相关的文本可能并不直接包含答案。本文提出一种新颖的RAG系统扩展方法，称为基于人工智能衍生文档的检索（RAIDD）。RAIDD通过在文档摄入阶段推导出推断特征（如摘要和示例问题），充分发挥大型语言模型在检索过程中的能力。实验证明，该方法能显著提升RAG系统在长上下文QA任务上的性能。