mlsanityData to 14 Oct 2026

Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs

arXiv 2404.00942 ↗First posted 1 Apr 2024cs.CL cs.AI cs.LGcited by 10

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Abstract (arXiv)

The advent of Large Language Models (LLMs) has significantly transformed the AI landscape, enhancing machine learning and AI capabilities. Factuality issue is a critical concern for LLMs, as they may generate factually incorrect responses. In this paper, we propose GraphEval to evaluate an LLM's performance using a substantially large test dataset. Specifically, the test dataset is retrieved from a large knowledge graph with more than 10 million facts without expensive human efforts. Unlike conventional methods that evaluate LLMs based on generated responses, GraphEval streamlines the evaluation process by creating a judge model to estimate the correctness of the answers given by the LLM. Our experiments demonstrate that the judge model's factuality assessment aligns closely with the correctness of the LLM's generated outputs, while also substantially reducing evaluation costs. Besides, our findings offer valuable insights into LLM performance across different metrics and highlight the potential for future improvements in ensuring the factual integrity of LLM outputs. The code is publicly available at https://github.com/xz-liu/GraphEval.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 56 of its 71 references that are in the map. First posted on arXiv 1 Apr 2024.

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56 of 71 references.

References · 56

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38 more
  1. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 698
  2. 2021P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Taskscited by 689
  3. 2023The Internal State of an LLM Knows When It's Lyingcited by 659
  4. 2023Bias and Fairness in Large Language Models: A Surveycited by 639
  5. 2023The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"cited by 407
  6. 2023Editing Large Language Models: Problems, Methods, and Opportunitiescited by 372
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  12. 2023Do Large Language Models Know What They Don't Know?cited by 243
  13. 2023Fine-tuning Language Models for Factualitycited by 221
  14. 2023Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificitycited by 206
  15. 2023Don't Make Your LLM an Evaluation Benchmark Cheatercited by 194
  16. 2020Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgettingcited by 170
  17. 2023Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs?cited by 168
  18. 2022Deep Bidirectional Language-Knowledge Graph Pretrainingcited by 156
  19. 2023Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentationcited by 147
  20. 2023Understanding Catastrophic Forgetting in Language Models via Implicit Inferencecited by 144
  21. 2016Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignmentcited by 128
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 4; 2026: 2.

10 papers.

  1. 2024Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Modelscited by 94
  2. 2024FlowBench: Revisiting and Benchmarking Workflow-Guided Planning for LLM-based Agentscited by 40
  3. 2024Towards Federated RLHF with Aggregated Client Preference for LLMscited by 13
  4. 2025GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checkingcited by 12
  5. 2024How Reliable are LLMs as Knowledge Bases? Re-thinking Facutality and Consistencycited by 10
  6. 2025ECKGBench: Benchmarking Large Language Models in E-commerce Leveraging Knowledge Graphcited by 6
  7. 2025Deliberate Planning in Language Models with Symbolic Representationcited by 3
  8. 2025KScope: A Framework for Characterizing the Knowledge Status of Language Modelscited by 3
  9. 2026KGHaluBench: A Knowledge Graph-Based Hallucination Benchmark for Evaluating the Breadth and Depth of LLM Knowledgecited by 0
  10. 2026FlatManifold: Robust Continual Learning under Severe Label Noise and Domain Shifts via Intrinsic Manifold Flatteningcited by 0