Mitigating LLM Hallucinations with Knowledge Graphs: A Case Study

• URL: http://arxiv.org/abs/2504.12422v1
• Date: Wed, 16 Apr 2025 18:40:01 GMT
• Title: Mitigating LLM Hallucinations with Knowledge Graphs: A Case Study
• Authors: Harry Li, Gabriel Appleby, Kenneth Alperin, Steven R Gomez, Ashley Suh,
• Abstract summary: LinkQ is an open-source natural language interface developed to combat hallucinations by forcing an LLM to query a knowledge graph for ground-truth data during question-answering (QA)<n>We conduct a quantitative evaluation of LinkQ using a well-known KGQA dataset, showing that the system outperforms GPT-4 but still struggles with certain question categories.<n>We discuss a qualitative study of LinkQ with two domain experts using a real-world cybersecurity KG, outlining these experts' feedback, suggestions, perceived limitations, and future opportunities for systems like LinkQ.
• Score: 2.40997250653065
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-stakes domains like cyber operations need responsible and trustworthy AI methods. While large language models (LLMs) are becoming increasingly popular in these domains, they still suffer from hallucinations. This research paper provides learning outcomes from a case study with LinkQ, an open-source natural language interface that was developed to combat hallucinations by forcing an LLM to query a knowledge graph (KG) for ground-truth data during question-answering (QA). We conduct a quantitative evaluation of LinkQ using a well-known KGQA dataset, showing that the system outperforms GPT-4 but still struggles with certain question categories - suggesting that alternative query construction strategies will need to be investigated in future LLM querying systems. We discuss a qualitative study of LinkQ with two domain experts using a real-world cybersecurity KG, outlining these experts' feedback, suggestions, perceived limitations, and future opportunities for systems like LinkQ.

Related papers

• Question-Aware Knowledge Graph Prompting for Enhancing Large Language Models [51.47994645529258]
  We propose Question-Aware Knowledge Graph Prompting (QAP), which incorporates question embeddings into GNN aggregation to dynamically assess KG relevance.<n> Experimental results demonstrate that QAP outperforms state-of-the-art methods across multiple datasets, highlighting its effectiveness.
  arXiv  Detail & Related papers  (2025-03-30T17:09:11Z)
• Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective [5.769786334333616]
  Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) based applications including automated text generation, question answering, and others. They face a significant challenge: hallucinations, where models produce plausible-sounding but factually incorrect responses. This paper discusses these open challenges covering state-of-the-art datasets and benchmarks as well as methods for knowledge integration and evaluating hallucinations.
  arXiv  Detail & Related papers  (2024-11-21T16:09:05Z)
• LinkQ: An LLM-Assisted Visual Interface for Knowledge Graph Question-Answering [1.5238808518078564]
  LinkQ is a system that leverages a large language model (LLM) to facilitate knowledge graph (KG) query construction through natural language question-answering.<n>LLM interprets a user's question, then systematically converts it into a well-formed query.<n>LinkQ guards against the LLM hallucinating outputs by ensuring users' questions are only ever answered from ground truth KG data.
  arXiv  Detail & Related papers  (2024-06-07T15:28:31Z)
• CuriousLLM: Elevating Multi-Document Question Answering with LLM-Enhanced Knowledge Graph Reasoning [0.9295048974480845]
  We propose CuriousLLM, an enhancement that integrates a curiosity-driven reasoning mechanism into an LLM agent.<n>This mechanism enables the agent to generate relevant follow-up questions, thereby guiding the information retrieval process more efficiently.<n>Our experiments show that CuriousLLM significantly boosts LLM performance in multi-document question answering (MD-QA)
  arXiv  Detail & Related papers  (2024-04-13T20:43:46Z)
• 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)
• Merging Generated and Retrieved Knowledge for Open-Domain QA [72.42262579925911]
  COMBO is a compatibility-Oriented knowledge Merging for Better Open-domain QA framework. We show that COMBO outperforms competitive baselines on three out of four tested open-domain QA benchmarks.
  arXiv  Detail & Related papers  (2023-10-22T19:37:06Z)
• Systematic Assessment of Factual Knowledge in Large Language Models [48.75961313441549]
  This paper proposes a framework to assess the factual knowledge of large language models (LLMs) by leveraging knowledge graphs (KGs) Our framework automatically generates a set of questions and expected answers from the facts stored in a given KG, and then evaluates the accuracy of LLMs in answering these questions.
  arXiv  Detail & Related papers  (2023-10-18T00:20:50Z)
• Won't Get Fooled Again: Answering Questions with False Premises [79.8761549830075]
  Pre-trained language models (PLMs) have shown unprecedented potential in various fields. PLMs tend to be easily deceived by tricky questions such as "How many eyes does the sun have?" We find that the PLMs already possess the knowledge required to rebut such questions.
  arXiv  Detail & Related papers  (2023-07-05T16:09:21Z)
• QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering [122.84513233992422]
  We propose a new model, QA-GNN, which addresses the problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) We show its improvement over existing LM and LM+KG models, as well as its capability to perform interpretable and structured reasoning.
  arXiv  Detail & Related papers  (2021-04-13T17:32:51Z)

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.