mlsanityData to 7 Oct 2026

Event-level Knowledge Editing

arXiv 2402.13093 ↗First posted 20 Feb 2024cs.CL cs.AIcited by 8

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Abstract (arXiv)

Knowledge editing aims at updating knowledge of large language models (LLMs) to prevent them from becoming outdated. Existing work edits LLMs at the level of factual knowledge triplets. However, natural knowledge updates in the real world come from the occurrences of new events rather than direct changes in factual triplets. In this paper, we propose a new task setting: event-level knowledge editing, which directly edits new events into LLMs and improves over conventional triplet-level editing on (1) Efficiency. A single event edit leads to updates in multiple entailed knowledge triplets. (2) Completeness. Beyond updating factual knowledge, event-level editing also requires considering the event influences and updating LLMs' knowledge about future trends. We construct a high-quality event-level editing benchmark ELKEN, consisting of 1,515 event edits, 6,449 questions about factual knowledge, and 10,150 questions about future tendencies. We systematically evaluate the performance of various knowledge editing methods and LLMs on this benchmark. We find that ELKEN poses significant challenges to existing knowledge editing approaches. Our codes and dataset are publicly released to facilitate further research.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 32 of its 50 references that are in the map. First posted on arXiv 20 Feb 2024.

Builds on

32 of 50 references.

References · 32

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  4. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  5. 2022Scaling Instruction-Finetuned Language Modelscited by 2,997
  6. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  7. 2022Locating and Editing Factual Associations in GPTcited by 2,609
  8. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,216
  9. 2022Mass-Editing Memory in a Transformercited by 976
  10. 2023ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debatecited by 724
  11. 2023An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuningcited by 622
  12. 2021Editing Factual Knowledge in Language Modelscited by 548
20 more
  1. 2021Fast Model Editing at Scalecited by 491
  2. 2022Memory-Based Model Editing at Scalecited by 443
  3. 2017Zero-Shot Relation Extraction via Reading Comprehensioncited by 425
  4. 2023Editing Large Language Models: Problems, Methods, and Opportunitiescited by 367
  5. 2023MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questionscited by 292
  6. 2023Can We Edit Factual Knowledge by In-Context Learning?cited by 284
  7. 2023Evaluating the Ripple Effects of Knowledge Editing in Language Modelscited by 238
  8. 2023Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Modelscited by 237
  9. 2020Modifying Memories in Transformer Modelscited by 222
  10. 2023Knowledge Editing for Large Language Models: A Surveycited by 189
  11. 2023Benchmarking Foundation Models with Language-Model-as-an-Examinercited by 172
  12. 2023PMET: Precise Model Editing in a Transformercited by 170
  13. 2020Editable Neural Networkscited by 154
  14. 2020MAVEN: A Massive General Domain Event Detection Datasetcited by 103
  15. 2022Forecasting Future World Events with Neural Networkscited by 63
  16. 2023ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustnesscited by 41
  17. 2023Evaluating the Reversal Curse in Model Editingcited by 34
  18. 2024Fast Exact Unlearning for In-Context Learning Data for LLMscited by 33
  19. 2023DocTER: Evaluating Document-based Knowledge Editingcited by 26
  20. 2023Assessing Knowledge Editing in Language Models via Relation Perspectivecited by 15

Built on by

Cited by year

Citing papers per year: 2024: 3; 2025: 5.

8 papers.

  1. 2024Can Knowledge Editing Really Correct Hallucinations?cited by 28
  2. 2025WikiBigEdit: Understanding the Limits of Lifelong Knowledge Editing in LLMscited by 25
  3. 2024Can Editing LLMs Inject Harm?cited by 22
  4. 2024Reasons and Solutions for the Decline in Model Performance after Editingcited by 7
  5. 2025ThinkEval: Practical Evaluation of Knowledge Leakage in LLM Editing using Thought-based Knowledge Graphscited by 4
  6. 2025Capability Localization: Capabilities Can be Localized rather than Individual Knowledgecited by 1
  7. 2025ScEdit: Script-based Assessment of Knowledge Editingcited by 1
  8. 2025Benchmarking and Rethinking Knowledge Editing for Large Language Modelscited by 0