Fine-grained Stateful Knowledge Exploration: Effective and Efficient Graph Retrieval with Large Language Models

• URL: http://arxiv.org/abs/2401.13444v4
• Date: Tue, 15 Jul 2025 02:25:49 GMT
• Title: Fine-grained Stateful Knowledge Exploration: Effective and Efficient Graph Retrieval with Large Language Models
• Authors: Dehao Tao, Congqi Wang, Feng Huang, Junhao Chen, Yongfeng Huang, Minghu Jiang,
• Abstract summary: Large Language Models (LLMs) have shown impressive capabilities, yet updating their knowledge remains a significant challenge.<n>Most existing methods use a paradigm that treats the whole question as the objective, with relevant knowledge being incrementally retrieved from the knowledge graph.<n>We propose FiSKE, a novel paradigm for Fine-grained Stateful Knowledge Exploration.
• Score: 19.049828741139425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have shown impressive capabilities, yet updating their knowledge remains a significant challenge, often leading to outdated or inaccurate responses. A proposed solution is the integration of external knowledge bases, such as knowledge graphs, with LLMs. Most existing methods use a paradigm that treats the whole question as the objective, with relevant knowledge being incrementally retrieved from the knowledge graph. However, this paradigm often leads to a granularity mismatch between the target question and the retrieved entities and relations. As a result, the information in the question cannot precisely correspond to the retrieved knowledge. This may cause redundant exploration or omission of vital knowledge, thereby leading to enhanced computational consumption and reduced retrieval accuracy. To address the limitations of coarse-grained knowledge exploration, we propose FiSKE, a novel paradigm for Fine-grained Stateful Knowledge Exploration. FiSKE first decomposes questions into fine-grained clues, then employs an adaptive mapping strategy during knowledge exploration process to resolve ambiguity in clue-to-graph mappings. This strategy dynamically infers contextual correspondences while maintaining a stateful record of the mappings. A clue-driven termination mechanism ensures rigorous augmentation--leveraging fully mapped paths for LLMs while reverting to chain-of-thought reasoning when necessary. Our approach balances precision and efficiency. Experiments on multiple datasets revealed that our paradigm surpasses current advanced methods in knowledge retrieval while significantly reducing the average number of LLM invocations.

Related papers

• Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities [43.88784275673178]
  Retrieval-Augmented Generation (RAG) shows impressive performance by supplementing and substituting parametric knowledge in Large Language Models (LLMs)<n>We show how triplets located in the gold reasoning path and their variants are used to construct partially relevant knowledge by removing the path that contains the answer.<n>Our awakening-based approach demonstrates greater efficacy in practical applications, outperforms traditional methods that rely on embedding-based similarity.
  arXiv  Detail & Related papers  (2025-08-02T09:54:46Z)
• PropMEND: Hypernetworks for Knowledge Propagation in LLMs [82.99849359892112]
  We present a hypernetwork-based approach for knowledge propagation, named PropMEND.<n>We show almost 2x accuracy on challenging multi-hop questions whose answers are not explicitly stated in the injected fact.<n>We also introduce a new dataset, Controlled RippleEdit, to evaluate the generalization of our hypernetwork.
  arXiv  Detail & Related papers  (2025-06-10T15:44:19Z)
• KnowTrace: Bootstrapping Iterative Retrieval-Augmented Generation with Structured Knowledge Tracing [64.38243807002878]
  We present KnowTrace, an elegant RAG framework to mitigate the context overload in large language models.<n>KnowTrace autonomously traces out desired knowledge triplets to organize a specific knowledge graph relevant to the input question.<n>It consistently surpasses existing methods across three multi-hop question answering benchmarks.
  arXiv  Detail & Related papers  (2025-05-26T17:22:20Z)
• Systematic Knowledge Injection into Large Language Models via Diverse Augmentation for Domain-Specific RAG [24.660769275714685]
  Retrieval-Augmented Generation (RAG) has emerged as a prominent method for incorporating domain knowledge into Large Language Models (LLMs) We present a novel framework that significantly enhances the fine-tuning process by augmenting the training data in two ways -- context augmentation and knowledge paraphrasing.
  arXiv  Detail & Related papers  (2025-02-12T12:39:51Z)
• LEKA:LLM-Enhanced Knowledge Augmentation [24.552995956148145]
  Humans excel in analogical learning and knowledge transfer. Models would transition from passively acquiring to actively accessing and learning from knowledge. We develop a knowledge augmentation method LEKA for knowledge transfer.
  arXiv  Detail & Related papers  (2025-01-29T17:44:57Z)
• Harnessing Large Language Models for Knowledge Graph Question Answering via Adaptive Multi-Aspect Retrieval-Augmentation [81.18701211912779]
  We introduce an Adaptive Multi-Aspect Retrieval-augmented over KGs (Amar) framework.<n>This method retrieves knowledge including entities, relations, and subgraphs, and converts each piece of retrieved text into prompt embeddings.<n>Our method has achieved state-of-the-art performance on two common datasets.
  arXiv  Detail & Related papers  (2024-12-24T16:38:04Z)
• KBAlign: Efficient Self Adaptation on Specific Knowledge Bases [75.78948575957081]
  Large language models (LLMs) usually rely on retrieval-augmented generation to exploit knowledge materials in an instant manner. We propose KBAlign, an approach designed for efficient adaptation to downstream tasks involving knowledge bases. Our method utilizes iterative training with self-annotated data such as Q&A pairs and revision suggestions, enabling the model to grasp the knowledge content efficiently.
  arXiv  Detail & Related papers  (2024-11-22T08:21:03Z)
• Exploring Knowledge Boundaries in Large Language Models for Retrieval Judgment [56.87031484108484]
  Large Language Models (LLMs) are increasingly recognized for their practical applications. Retrieval-Augmented Generation (RAG) tackles this challenge and has shown a significant impact on LLMs. By minimizing retrieval requests that yield neutral or harmful results, we can effectively reduce both time and computational costs.
  arXiv  Detail & Related papers  (2024-11-09T15:12:28Z)
• Combining LLMs and Knowledge Graphs to Reduce Hallucinations in Question Answering [0.0]
  Large Language Models (LLM) and Knowledge Graphs (KG) are combined to improve the accuracy and reliability of question-answering systems. Our method incorporates a query checker that ensures the syntactical and semantic validity of LLM-generated queries. To make this approach accessible, a user-friendly web-based interface has been developed.
  arXiv  Detail & Related papers  (2024-09-06T10:49:46Z)
• Knowledge Graph-Enhanced Large Language Models via Path Selection [58.228392005755026]
  Large Language Models (LLMs) have shown unprecedented performance in various real-world applications. LLMs are known to generate factually inaccurate outputs, a.k.a. the hallucination problem. We propose a principled framework KELP with three stages to handle the above problems.
  arXiv  Detail & Related papers  (2024-06-19T21:45:20Z)
• Can Graph Learning Improve Planning in LLM-based Agents? [61.47027387839096]
  Task planning in language agents is emerging as an important research topic alongside the development of large language models (LLMs) In this paper, we explore graph learning-based methods for task planning, a direction that is to the prevalent focus on prompt design. Our interest in graph learning stems from a theoretical discovery: the biases of attention and auto-regressive loss impede LLMs' ability to effectively navigate decision-making on graphs.
  arXiv  Detail & Related papers  (2024-05-29T14:26:24Z)
• Infusing Knowledge into Large Language Models with Contextual Prompts [5.865016596356753]
  We propose a simple yet generalisable approach for knowledge infusion by generating prompts from the context in the input text. Our experiments show the effectiveness of our approach which we evaluate by probing the fine-tuned LLMs.
  arXiv  Detail & Related papers  (2024-03-03T11:19:26Z)
• InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration [58.61492157691623]
  Methods for integrating knowledge have been developed, which augment LLMs with domain-specific knowledge graphs through external modules. Our research focuses on a novel problem: efficiently integrating unknown knowledge into LLMs without unnecessary overlap of known knowledge. A risk of introducing new knowledge is the potential forgetting of existing knowledge.
  arXiv  Detail & Related papers  (2024-02-18T03:36:26Z)
• Query of CC: Unearthing Large Scale Domain-Specific Knowledge from Public Corpora [104.16648246740543]
  We propose an efficient data collection method based on large language models. The method bootstraps seed information through a large language model and retrieves related data from public corpora. It not only collects knowledge-related data for specific domains but unearths the data with potential reasoning procedures.
  arXiv  Detail & Related papers  (2024-01-26T03:38:23Z)
• Context Matters: Pushing the Boundaries of Open-Ended Answer Generation with Graph-Structured Knowledge Context [4.1229332722825]
  This paper introduces a novel framework that combines graph-driven context retrieval in conjunction to knowledge graphs based enhancement. We conduct experiments on various Large Language Models (LLMs) with different parameter sizes to evaluate their ability to ground knowledge and determine factual accuracy in answers to open-ended questions. Our methodology GraphContextGen consistently outperforms dominant text-based retrieval systems, demonstrating its robustness and adaptability to a larger number of use cases.
  arXiv  Detail & Related papers  (2024-01-23T11:25:34Z)
• DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text [73.68051228972024]
  Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge. Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
  arXiv  Detail & Related papers  (2023-10-31T04:37:57Z)
• Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators [78.63553017938911]
  Large language models (LLMs) outperform information retrieval techniques for downstream knowledge-intensive tasks. However, community concerns abound regarding the factuality and potential implications of using this uncensored knowledge. We introduce CONNER, designed to evaluate generated knowledge from six important perspectives.
  arXiv  Detail & Related papers  (2023-10-11T08:22:37Z)
• Self-Knowledge Guided Retrieval Augmentation for Large Language Models [59.771098292611846]
  Large language models (LLMs) have shown superior performance without task-specific fine-tuning. Retrieval-based methods can offer non-parametric world knowledge and improve the performance on tasks such as question answering. Self-Knowledge guided Retrieval augmentation (SKR) is a simple yet effective method which can let LLMs refer to the questions they have previously encountered.
  arXiv  Detail & Related papers  (2023-10-08T04:22:33Z)
• Graph Neural Prompting with Large Language Models [32.97391910476073]
  Graph Neural Prompting (GNP) is a novel plug-and-play method to assist pre-trained language models in learning beneficial knowledge from knowledge graphs. Extensive experiments on multiple datasets demonstrate the superiority of GNP on both commonsense and biomedical reasoning tasks.
  arXiv  Detail & Related papers  (2023-09-27T06:33:29Z)
• Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations [63.19448893196642]
  We present a novel framework that harnesses the rich contextual information and semantic representations provided by large language models to analyze behavior graphs. By leveraging this capability, our framework enables personalized and accurate job recommendations for individual users.
  arXiv  Detail & Related papers  (2023-07-10T11:29:41Z)
• Large Language Models Can Be Easily Distracted by Irrelevant Context [29.315230178997002]
  We investigate how the model problem-solving accuracy can be influenced by irrelevant context. We use benchmark to measure the distractibility of cutting-edge prompting techniques for large language models.
  arXiv  Detail & Related papers  (2023-01-31T20:48:57Z)
• A Unified End-to-End Retriever-Reader Framework for Knowledge-based VQA [67.75989848202343]
  This paper presents a unified end-to-end retriever-reader framework towards knowledge-based VQA. We shed light on the multi-modal implicit knowledge from vision-language pre-training models to mine its potential in knowledge reasoning. Our scheme is able to not only provide guidance for knowledge retrieval, but also drop these instances potentially error-prone towards question answering.
  arXiv  Detail & Related papers  (2022-06-30T02:35:04Z)
• BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models [65.51390418485207]
  We propose a new approach of harvesting massive KGs of arbitrary relations from pretrained LMs. With minimal input of a relation definition, the approach efficiently searches in the vast entity pair space to extract diverse accurate knowledge. We deploy the approach to harvest KGs of over 400 new relations from different LMs.
  arXiv  Detail & Related papers  (2022-06-28T19:46:29Z)
• Incremental Knowledge Based Question Answering [52.041815783025186]
  We propose a new incremental KBQA learning framework that can progressively expand learning capacity as humans do. Specifically, it comprises a margin-distilled loss and a collaborative selection method, to overcome the catastrophic forgetting problem. The comprehensive experiments demonstrate its effectiveness and efficiency when working with the evolving knowledge base.
  arXiv  Detail & Related papers  (2021-01-18T09:03:38Z)
• KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA [107.7091094498848]
  One of the most challenging question types in VQA is when answering the question requires outside knowledge not present in the image. In this work we study open-domain knowledge, the setting when the knowledge required to answer a question is not given/annotated, neither at training nor test time. We tap into two types of knowledge representations and reasoning. First, implicit knowledge which can be learned effectively from unsupervised language pre-training and supervised training data with transformer-based models.
  arXiv  Detail & Related papers  (2020-12-20T20:13:02Z)

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.