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

TL;DR

This paper introduces PROPEX-RAG, a prompt-driven graph retrieval framework that significantly improves multi-hop question answering by emphasizing prompt design and entity-based graph traversal, achieving state-of-the-art results.

Contribution

The paper presents a novel prompt-driven GraphRAG framework that leverages prompt formulation and entity-guided graph traversal for enhanced multi-hop reasoning and retrieval.

Findings

Achieved state-of-the-art F1 scores on HotpotQA and 2WikiMultiHopQA datasets.

Demonstrated the importance of prompt design in improving retrieval accuracy.

Showed that entity-guided graph traversal enhances multi-hop reasoning efficiency.

Abstract

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.