Improving Factuality in LLMs via Inference-Time Knowledge Graph Construction

• URL: http://arxiv.org/abs/2509.03540v2
• Date: Tue, 07 Oct 2025 20:15:22 GMT
• Title: Improving Factuality in LLMs via Inference-Time Knowledge Graph Construction
• Authors: Shanglin Wu, Lihui Liu, Jinho D. Choi, Kai Shu,
• Abstract summary: Large Language Models (LLMs) often struggle with producing factually consistent answers due to limitations in their parametric memory.<n>We propose a novel framework that dynamically constructs and expands knowledge graphs (KGs) during inference.
• Score: 30.381290814338413
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) often struggle with producing factually consistent answers due to limitations in their parametric memory. Retrieval-Augmented Generation (RAG) paradigms mitigate this issue by incorporating external knowledge at inference time. However, such methods typically handle knowledge as unstructured text, which reduces retrieval accuracy, hinders compositional reasoning, and amplifies the influence of irrelevant information on the factual consistency of LLM outputs. To overcome these limitations, we propose a novel framework that dynamically constructs and expands knowledge graphs (KGs) during inference, integrating both internal knowledge extracted from LLMs and external knowledge retrieved from external sources. Our method begins by extracting a seed KG from the question via prompting, followed by iterative expansion using the LLM's internal knowledge. The KG is then selectively refined through external retrieval, enhancing factual coverage and correcting inaccuracies. We evaluate our approach on three diverse Factual QA benchmarks, demonstrating consistent gains in factual accuracy over baselines. Our findings reveal that inference-time KG construction is a promising direction for enhancing LLM factuality in a structured, interpretable, and scalable manner.

Related papers

• Hindsight Distillation Reasoning with Knowledge Encouragement Preference for Knowledge-based Visual Question Answering [55.368681418311894]
  Existing Knowledge-based Visual Question Answering (KBVQA) methods either utilize implicit knowledge in multimodal large language models (MLLMs) via in-context learning or explicit knowledge via retrieval augmented generation.<n>We provide a Hindsight Distilled Reasoning (HinD) framework with Knowledge Encouragement Preference Optimization (KEPO)<n> Experiments on OK-VQA and A-OKVQA validate the effectiveness of HinD, showing that HinD with elicited reasoning from 7B-size MLLM achieves superior performance without commercial model APIs or outside knowledge.
  arXiv  Detail & Related papers  (2025-11-14T10:03:23Z)
• TruthfulRAG: Resolving Factual-level Conflicts in Retrieval-Augmented Generation with Knowledge Graphs [10.861331756147477]
  We propose TruthfulRAG to resolve factual-level knowledge conflicts in RAG systems.<n>TruthfulRAG constructs Knowledge Graphs (KGs) by systematically extracting triples from retrieved content.<n>Extensive experiments reveal that TruthfulRAG outperforms existing methods.
  arXiv  Detail & Related papers  (2025-11-13T14:49:44Z)
• TRAIL: Joint Inference and Refinement of Knowledge Graphs with Large Language Models [5.678291291711662]
  TRAIL is a novel, unified framework for Thinking, Reasoning, And Incremental Learning.<n>It couples joint inference and dynamic KG refinement with large language models.<n>Extensive experiments on multiple benchmarks demonstrate that TRAIL outperforms existing KG-augmented and retrieval-augmented LLM baselines by 3% to 13%.
  arXiv  Detail & Related papers  (2025-08-06T14:25:05Z)
• LLM Inference Enhanced by External Knowledge: A Survey [16.319049759753106]
  This study explores strategies for using external knowledge to enhance large language models (LLMs)<n>Our comparative analysis highlights the trade-offs among interpretability, scalability, and performance.
  arXiv  Detail & Related papers  (2025-05-30T09:08:51Z)
• 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)
• GenKI: Enhancing Open-Domain Question Answering with Knowledge Integration and Controllable Generation in Large Language Models [75.25348392263676]
  Open-domain question answering (OpenQA) represents a cornerstone in natural language processing (NLP)<n>We propose a novel framework named GenKI, which aims to improve the OpenQA performance by exploring Knowledge Integration and controllable Generation.
  arXiv  Detail & Related papers  (2025-05-26T08:18:33Z)
• Accommodate Knowledge Conflicts in Retrieval-augmented LLMs: Towards Reliable Response Generation in the Wild [11.058848731627233]
  Large language models (LLMs) have advanced information retrieval systems.<n>LLMs often face knowledge conflicts between internal memory and retrievaled external information.<n>We propose Swin-VIB, a novel framework that integrates a pipeline of variational information bottleneck models into adaptive augmentation of retrieved information.
  arXiv  Detail & Related papers  (2025-04-17T14:40:31Z)
• Knowledge Graph-extended Retrieval Augmented Generation for Question Answering [10.49712834719005]
  This paper proposes a system that integrates Large Language Models (LLMs) and Knowledge Graphs (KGs) without requiring training.<n>The resulting approach can be classified as a specific form of a Retrieval Augmented Generation (RAG) with a KG.<n>It includes a question decomposition module to enhance multi-hop information retrieval and answerability.
  arXiv  Detail & Related papers  (2025-04-11T18:03:02Z)
• Oreo: A Plug-in Context Reconstructor to Enhance Retrieval-Augmented Generation [28.568010424711563]
  Retrieval-Augmented Generation (RAG) aims to augment the capabilities of Large Language Models (LLMs)<n>We introduce a compact, efficient, and pluggable module designed to refine retrieved chunks before using them for generation.
  arXiv  Detail & Related papers  (2025-02-18T16:38:39Z)
• 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)
• KBM: Delineating Knowledge Boundary for Adaptive Retrieval in Large Language Models [69.99274367773997]
  Large Language Models (LLMs) often struggle with dynamically changing knowledge and handling unknown static information.<n>Retrieval-Augmented Generation (RAG) is employed to tackle these challenges and has a significant impact on improving LLM performance.<n>We propose a Knowledge Boundary Model (KBM) to express the known/unknown of a given question, and to determine whether a RAG needs to be triggered.
  arXiv  Detail & Related papers  (2024-11-09T15:12:28Z)
• GIVE: Structured Reasoning of Large Language Models with Knowledge Graph Inspired Veracity Extrapolation [108.2008975785364]
  Graph Inspired Veracity Extrapolation (GIVE) is a novel reasoning method that merges parametric and non-parametric memories to improve accurate reasoning with minimal external input.<n>GIVE guides the LLM agent to select the most pertinent expert data (observe), engage in query-specific divergent thinking (reflect), and then synthesize this information to produce the final output (speak)
  arXiv  Detail & Related papers  (2024-10-11T03:05:06Z)
• A Comprehensive Study of Knowledge Editing for Large Language Models [82.65729336401027]
  Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. This paper defines the knowledge editing problem and provides a comprehensive review of cutting-edge approaches. We introduce a new benchmark, KnowEdit, for a comprehensive empirical evaluation of representative knowledge editing approaches.
  arXiv  Detail & Related papers  (2024-01-02T16:54:58Z)
• Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofitting [51.7049140329611]
  This paper proposes Knowledge Graph-based Retrofitting (KGR) to mitigate factual hallucination during the reasoning process. Experiments show that KGR can significantly improve the performance of LLMs on factual QA benchmarks.
  arXiv  Detail & Related papers  (2023-11-22T11:08:38Z)
• Empowering Language Models with Knowledge Graph Reasoning for Question Answering [117.79170629640525]
  We propose knOwledge REasOning empowered Language Model (OREO-LM) OREO-LM consists of a novel Knowledge Interaction Layer that can be flexibly plugged into existing Transformer-based LMs. We show significant performance gain, achieving state-of-art results in the Closed-Book setting.
  arXiv  Detail & Related papers  (2022-11-15T18:26:26Z)

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.