mlsanityData to 7 Oct 2026

PROPEX-RAG: Enhanced GraphRAG using Prompt-Driven Prompt Execution

arXiv 2511.01802 ↗First posted 3 Nov 2025cs.CVcited by 0

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

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) has become a robust framework for enhancing Large Language Models (LLMs) with external knowledge. Recent advances in RAG have investigated graph based retrieval for intricate reasoning; however, the influence of prompt design on enhancing the retrieval and reasoning process is still considerably under-examined. In this paper, we present a prompt-driven GraphRAG framework that underscores the significance of prompt formulation in facilitating entity extraction, fact selection, and passage reranking for multi-hop question answering. Our approach creates a symbolic knowledge graph from text data by encoding entities and factual relationships as structured facts triples. We use LLMs selectively during online retrieval to perform semantic filtering and answer generation. We also use entity-guided graph traversal through Personalized PageRank (PPR) to support efficient, scalable retrieval based on the knowledge graph we built. Our system gets state-of-the-art performance on HotpotQA and 2WikiMultiHopQA, with F1 scores of 80.7% and 78.9%, and Recall@5 scores of 97.1% and 98.1%, respectively. These results show that prompt design is an important part of improving retrieval accuracy and response quality. This research lays the groundwork for more efficient and comprehensible multi-hop question-answering systems, highlighting the importance of prompt-aware graph reasoning.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. Builds on 18 of its 23 references that are in the map. First posted on arXiv 3 Nov 2025.

Builds on

18 of 23 references.

Closest ancestors · 7

Same direction, newest first

  1. 2025Query-Aware Graph Neural Networks for Enhanced Retrieval-Augmented Generation25 Jul
  2. 2025NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes15 Apr
  3. 2025Retrieval-Augmented Generation with Hierarchical Knowledge13 Mar
  4. 2024LightRAG: Simple and Fast Retrieval-Augmented Generation8 Oct
  5. 2024Graph Retrieval-Augmented Generation: A Survey15 Aug
  6. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarization24 Apr
  7. 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval31 Jan

Other references · 11

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  4. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  5. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,259
  6. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,203
  7. 2024NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Modelscited by 466
  8. 2021Large Dual Encoders Are Generalizable Retrieverscited by 454
  9. 2024Generative Representational Instruction Tuningcited by 227
  10. 2025From RAG to Memory: Non-Parametric Continual Learning for Large Language Modelscited by 223
  11. 2020Answering Complex Open-Domain Questions with Multi-Hop Dense Retrievalcited by 160

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  1. 2025Beyond Static Retrieval: Opportunities and Pitfalls of Iterative Retrieval in GraphRAG
  2. 2026Efficient Retrieval-Augmented Generation via Token Co-occurrence Graphs
  3. 2025Query-Centric Graph Retrieval Augmented Generation
  4. 2025PathRAG: Pruning Graph-based Retrieval Augmented Generation with Relational Paths
  5. 2026ContextRAG: Extraction-Free Hierarchical Graph Construction for Retrieval-Augmented Generation
  6. 2026Evaluating Prompt Engineering Techniques for RAG in Small Language Models: A Multi-Hop QA Approach
  7. 2026Use Graph When It Needs: Efficiently and Adaptively Integrating Retrieval-Augmented Generation with Graphs
  8. 2025You Don't Need Pre-built Graphs for RAG: Retrieval Augmented Generation with Adaptive Reasoning Structures