Question-Adaptive Graph Learning for Multi-hop Retrieval Augmented Generation

📰 ArXiv cs.AI

Learn to improve Retrieval-Augmented Generation with Question-Adaptive Graph Learning for multi-hop questions

advanced Published 28 Apr 2026

Action Steps

Build a graph learning model to adapt to question semantics
Run multi-hop retrieval experiments to evaluate the model's performance
Configure the model to integrate external knowledge sources
Test the model on complex questions with multiple knowledge targets
Apply Question-Adaptive Graph Learning to improve retrieval accuracy

Who Needs to Know This

NLP engineers and researchers can benefit from this technique to enhance their LLMs with external knowledge sources, especially for complex multi-hop questions

Key Insight

💡 Question-Adaptive Graph Learning can enhance LLMs by better understanding complex question semantics

Key Takeaways

Learn to improve Retrieval-Augmented Generation with Question-Adaptive Graph Learning for multi-hop questions

Full Article

Title: Question-Adaptive Graph Learning for Multi-hop Retrieval Augmented Generation

Abstract:
arXiv:2510.11541v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) has demonstrated its ability to enhance Large Language Models (LLMs) by integrating external knowledge sources. However, multi-hop questions, which require the identification of multiple knowledge targets to form a synthesized answer, raise new challenges for RAG systems. Under the multi-hop settings, existing methods often struggle to fully understand the questions with complex semantic structures and

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