KAQG: A Knowledge-Graph-Enhanced RAG for Difficulty-Controlled Question Generation

• URL: http://arxiv.org/abs/2505.07618v1
• Date: Mon, 12 May 2025 14:42:19 GMT
• Title: KAQG: A Knowledge-Graph-Enhanced RAG for Difficulty-Controlled Question Generation
• Authors: Ching Han Chen, Ming Fang Shiu,
• Abstract summary: KAQG introduces a decisive breakthrough for Retrieval-Augmented Generation (RAG)<n>It tackles the two chronic weaknesses of current pipelines: transparent multi-step reasoning and fine-grained cognitive difficulty control.<n>Technically, the framework fuses knowledge graphs, RAG retrieval, and educational assessment theory into a single pipeline.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: KAQG introduces a decisive breakthrough for Retrieval-Augmented Generation (RAG) by explicitly tackling the two chronic weaknesses of current pipelines: transparent multi-step reasoning and fine-grained cognitive difficulty control. This transforms RAG from a passive retriever into an accountable generator of calibrated exam items. Technically, the framework fuses knowledge graphs, RAG retrieval, and educational assessment theory into a single pipeline. Domain passages are parsed into a structured graph; graph-aware retrieval feeds fact chains to an LLM; and an assessment layer governed by Bloom's Taxonomy levels and Item Response Theory (IRT) transforms those chains into psychometrically sound questions. This cross-disciplinary marriage yields two scholarly contributions: it shows how semantic graph contexts guide LLM reasoning paths, and it operationalizes difficulty metrics within the generation process, producing items whose IRT parameters match expert benchmarks. Every module, from KG construction scripts to the multi-agent reasoning scheduler and the automatic IRT validator, is openly released on GitHub. This enables peer laboratories to replicate experiments, benchmark against baselines, and extend individual components without licensing barriers. Its reproducible design paves the way for rigorous ablation studies, cross-domain transfer experiments, and shared leaderboards on multi-step reasoning benchmarks.

Related papers

• Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning [20.05893083101089]
  Graph-R1 is an agentic GraphRAG framework via end-to-end reinforcement learning (RL)<n>It introduces lightweight knowledge hypergraph construction, models retrieval as a multi-turn agent-environment interaction.<n>Experiments on standard RAG datasets show that Graph-R1 outperforms traditional GraphRAG and RL-enhanced RAG methods in reasoning accuracy, retrieval efficiency, and generation quality.
  arXiv  Detail & Related papers  (2025-07-29T15:01:26Z)
• LTRR: Learning To Rank Retrievers for LLMs [53.285436927963865]
  We show that routing-based RAG systems can outperform the best single-retriever-based systems.<n>Performance gains are especially pronounced in models trained with the Answer Correctness (AC) metric.<n>As part of the SIGIR 2025 LiveRAG challenge, our submitted system demonstrated the practical viability of our approach.
  arXiv  Detail & Related papers  (2025-06-16T17:53:18Z)
• Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering [75.12322966980003]
  Large Language Models (LLMs) have shown strong inductive reasoning ability across various domains.<n>Most existing RAG pipelines rely on unstructured text, limiting interpretability and structured reasoning.<n>Recent studies have explored integrating knowledge graphs with LLMs for knowledge graph question answering.<n>We propose RAPL, a novel framework for efficient and effective graph retrieval in KGQA.
  arXiv  Detail & Related papers  (2025-06-11T12:03:52Z)
• CC-RAG: Structured Multi-Hop Reasoning via Theme-Based Causal Graphs [23.587337743113228]
  Causal-Chain RAG (CC-RAG) is a novel approach that integrates zero-shot triple extraction and theme-aware graph chaining into the RAG pipeline.<n>Given a domain specific corpus, CC-RAG constructs a Directed Acyclic Graph (DAG) of cause, relation, effect> triples and uses forward/backward chaining to guide structured answer generation.
  arXiv  Detail & Related papers  (2025-06-10T02:22:32Z)
• Align-GRAG: Reasoning-Guided Dual Alignment for Graph Retrieval-Augmented Generation [75.9865035064794]
  Large language models (LLMs) have demonstrated remarkable capabilities, but still struggle with issues like hallucinations and outdated information.<n>Retrieval-augmented generation (RAG) addresses these issues by grounding LLM outputs in external knowledge with an Information Retrieval (IR) system.<n>We propose Align-GRAG, a novel reasoning-guided dual alignment framework in post-retrieval phrase.
  arXiv  Detail & Related papers  (2025-05-22T05:15:27Z)
• Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning [18.96570718233786]
  SPLIT-RAG is a multi-agent RAG framework that addresses the limitations with question-driven semantic graph partitioning and collaborative subgraph retrieval.<n>The innovative framework first create Semantic Partitioning of Linked Information, then use the Type-Specialized knowledge base to achieve Multi-Agent RAG.<n>The attribute-aware graph segmentation manages to divide knowledge graphs into semantically coherent subgraphs, ensuring subgraphs align with different query types.<n>A hierarchical merging module resolves inconsistencies across subgraph-derived answers through logical verifications.
  arXiv  Detail & Related papers  (2025-05-20T06:44:34Z)
• AlignRAG: An Adaptable Framework for Resolving Misalignments in Retrieval-Aware Reasoning of RAG [61.28113271728859]
  Retrieval-augmented generation (RAG) has emerged as a foundational paradigm for knowledge-grounded text generation.<n>Existing RAG pipelines often fail to ensure that the reasoning trajectories align with the evidential constraints imposed by retrieved content.<n>We propose AlignRAG, a novel test-time framework that mitigates reasoning misalignment through iterative Critique-Driven Alignment steps.
  arXiv  Detail & Related papers  (2025-04-21T04:56:47Z)
• Chain-of-Retrieval Augmented Generation [72.06205327186069]
  This paper introduces an approach for training o1-like RAG models that retrieve and reason over relevant information step by step before generating the final answer.<n>Our proposed method, CoRAG, allows the model to dynamically reformulate the query based on the evolving state.
  arXiv  Detail & Related papers  (2025-01-24T09:12:52Z)
• Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation [9.844598565914055]
  Large Language Models (LLMs) demonstrate strong reasoning abilities but face limitations such as hallucinations and outdated knowledge.<n>We introduce SubgraphRAG, extending the Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) framework that retrieves subgraphs.<n>Our approach innovatively integrates a lightweight multilayer perceptron with a parallel triple-scoring mechanism for efficient and flexible subgraph retrieval.
  arXiv  Detail & Related papers  (2024-10-28T04:39:32Z)
• Debate on Graph: a Flexible and Reliable Reasoning Framework for Large Language Models [33.662269036173456]
  Large Language Models (LLMs) may suffer from hallucinations in real-world applications due to the lack of relevant knowledge. Knowledge Graph Question Answering (KGQA) serves as a critical touchstone for the integration. We propose an interactive KGQA framework that leverages the interactive learning capabilities of LLMs to perform reasoning and Debating over Graphs (DoG)
  arXiv  Detail & Related papers  (2024-09-05T01:11:58Z)
• RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework [66.93260816493553]
  This paper introduces RAGEval, a framework designed to assess RAG systems across diverse scenarios.<n>With a focus on factual accuracy, we propose three novel metrics: Completeness, Hallucination, and Irrelevance.<n> Experimental results show that RAGEval outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples.
  arXiv  Detail & Related papers  (2024-08-02T13:35:11Z)
• TRACE the Evidence: Constructing Knowledge-Grounded Reasoning Chains for Retrieval-Augmented Generation [30.485127201645437]
  We propose TRACE to enhance the multi-hop reasoning ability of RAG models. TRACE constructs knowledge-grounded reasoning chains, which are a series of logically connected knowledge triples. TRACE achieves an average performance improvement of up to 14.03% compared to using all the retrieved documents.
  arXiv  Detail & Related papers  (2024-06-17T12:23:32Z)
• RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation [42.82192656794179]
  Large Language Models (LLMs) exhibit remarkable capabilities but are prone to generating inaccurate or hallucinatory responses. This limitation stems from their reliance on vast pretraining datasets, making them susceptible to errors in unseen scenarios. Retrieval-Augmented Generation (RAG) addresses this by incorporating external, relevant documents into the response generation process.
  arXiv  Detail & Related papers  (2024-03-31T08:58:54Z)
• Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection [74.51523859064802]
  We introduce a new framework called Self-Reflective Retrieval-Augmented Generation (Self-RAG) Self-RAG enhances an LM's quality and factuality through retrieval and self-reflection. It significantly outperforms state-of-the-art LLMs and retrieval-augmented models on a diverse set of tasks.
  arXiv  Detail & Related papers  (2023-10-17T18:18:32Z)
• Unsupervised Controllable Generation with Self-Training [90.04287577605723]
  controllable generation with GANs remains a challenging research problem. We propose an unsupervised framework to learn a distribution of latent codes that control the generator through self-training. Our framework exhibits better disentanglement compared to other variants such as the variational autoencoder.
  arXiv  Detail & Related papers  (2020-07-17T21:50:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.