mlsanityData to 14 Oct 2026

KGiRAG: An Iterative GraphRAG Approach for Responding Sensemaking Queries

arXiv 2604.20859 ↗First posted 2 Mar 2026cs.IR cs.AI cs.CLcited by 0

On the map: Retrieval-augmented generation → Knowledge-graph and structured-knowledge RAG

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. Builds on 15 of its 19 references that are in the map. First posted on arXiv 2 Mar 2026.

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15 of 19 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models21 Jan
  2. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)31 Dec
  3. 2024Entity Retrieval for Answering Entity-Centric Questions5 Aug
  4. 2024GRAG: Graph Retrieval-Augmented Generation26 May
  5. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarization24 Apr

Other references · 10

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  3. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  4. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  5. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
  6. 2022Large Language Models Struggle to Learn Long-Tail Knowledgecited by 458
  7. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 418
  8. 2022RARR: Researching and Revising What Language Models Say, Using Language Modelscited by 287
  9. 2023GraphGPT: Graph Instruction Tuning for Large Language Modelscited by 226
  10. 2024DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAGcited by 4

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Similar, not cited

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  1. 2025KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering
  2. 2026UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG
  3. 2025NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes
  4. 2024Mindful-RAG: A Study of Points of Failure in Retrieval Augmented Generation
  5. 2024Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation
  6. 2025Beyond Static Retrieval: Opportunities and Pitfalls of Iterative Retrieval in GraphRAG
  7. 2025Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation
  8. 2025PathRAG: Pruning Graph-based Retrieval Augmented Generation with Relational Paths