KGiRAG：一种用于应答意义生成查询的迭代图RAG方法
KGiRAG: An Iterative GraphRAG Approach for Responding Sensemaking Queries
AI总结：
本文提出KGiRAG，一种基于图的迭代RAG方法，通过反馈机制迭代优化输出，提升复杂查询的语义质量和相关性。
摘要：
近期文献强调了基于图的方法在大型语言模型（LLM）检索增强生成（RAG）管道中回答复杂查询的潜力，特别是那些超出LLM先验知识的查询。然而，LLM容易产生幻觉，并在处理足够大的上下文以有效支撑复杂查询时面临技术限制。为了解决这些挑战，我们提出了一种新颖的迭代、反馈驱动的图RAG架构，利用响应质量评估来迭代优化输出，直到产生一个坚实、有依据的响应。通过使用HotPotQA数据集中的查询评估我们的方法，我们证明这种迭代RAG策略产生的响应在语义质量和相关性方面优于单次基线。
英文摘要：
Recent literature highlights the potential of graph-based approaches within large language model (LLM) retrieval-augmented generation (RAG) pipelines for answering queries of varying complexity, particularly those that fall outside the LLM's prior knowledge. However, LLMs are prone to hallucination and often face technical limitations in handling contexts large enough to ground complex queries effectively. To address these challenges, we propose a novel iterative, feedback-driven GraphRAG architecture that leverages response quality assessment to iteratively refine outputs until a sound, well-grounded response is produced. Evaluating our approach with queries from the HotPotQA dataset, we demonstrate that this iterative RAG strategy yields responses with higher semantic quality and improved relevance compared to a single-shot baseline.
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