mlsanityData to 7 Oct 2026

CKnowEdit: A New Chinese Knowledge Editing Dataset for Linguistics, Facts, and Logic Error Correction in LLMs

arXiv 2409.05806 ↗First posted 9 Sep 2024cs.CL cs.AI cs.IRcited by 3

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Abstract (arXiv)

Chinese, as a linguistic system rich in depth and complexity, is characterized by distinctive elements such as ancient poetry, proverbs, idioms, and other cultural constructs. However, current Large Language Models (LLMs) face limitations in these specialized domains, highlighting the need for the development of comprehensive datasets that can assess, continuously update, and progressively improve these culturally-grounded linguistic competencies through targeted training optimizations. To address this gap, we introduce CKnowEdit, the first-ever Chinese knowledge editing dataset designed to correct linguistic, factual, and logical errors in LLMs. We collect seven types of knowledge from a wide range of sources, including classical texts, idioms, and content from Baidu Tieba Ruozhiba, taking into account the unique polyphony, antithesis, and logical structures inherent in the Chinese language. By analyzing this dataset, we highlight the challenges current LLMs face in mastering Chinese. Furthermore, our evaluation of state-of-the-art knowledge editing techniques reveals opportunities to advance the correction of Chinese knowledge. Code and dataset are available at https://github.com/zjunlp/EasyEdit.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 26 of its 45 references that are in the map. First posted on arXiv 9 Sep 2024.

Builds on

26 of 45 references.

References · 26

  1. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  2. 2022Locating and Editing Factual Associations in GPTcited by 2,609
  3. 2022Mass-Editing Memory in a Transformercited by 976
  4. 2023Baichuan 2: Open Large-scale Language Modelscited by 664
  5. 2021Editing Factual Knowledge in Language Modelscited by 548
  6. 2022Memory-Based Model Editing at Scalecited by 443
  7. 2023Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignmentcited by 394
  8. 2023Editing Large Language Models: Problems, Methods, and Opportunitiescited by 367
  9. 2023MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questionscited by 292
  10. 2023Can We Edit Factual Knowledge by In-Context Learning?cited by 284
  11. 2022Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptorscited by 267
  12. 2023Transformer-Patcher: One Mistake worth One Neuroncited by 196
14 more
  1. 2023Knowledge Editing for Large Language Models: A Surveycited by 189
  2. 2022Calibrating Factual Knowledge in Pretrained Language Modelscited by 92
  3. 2024BadEdit: Backdooring large language models by model editingcited by 84
  4. 2023SuperCLUE: A Comprehensive Chinese Large Language Model Benchmarkcited by 50
  5. 2019Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translationcited by 45
  6. 2024COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuningcited by 45
  7. 2024AKEW: Assessing Knowledge Editing in the Wildcited by 35
  8. 2023Separate the Wheat from the Chaff: Model Deficiency Unlearning via Parameter-Efficient Module Operationcited by 33
  9. 2024DeepEdit: Knowledge Editing as Decoding with Constraintscited by 33
  10. 2024Fundamental Problems With Model Editing: How Should Rational Belief Revision Work in LLMs?cited by 24
  11. 2024Long-form evaluation of model editingcited by 23
  12. 2024Benchmarking Chinese Commonsense Reasoning of LLMs: From Chinese-Specifics to Reasoning-Memorization Correlationscited by 14
  13. 2024BMIKE-53: Investigating Cross-Lingual Knowledge Editing with In-Context Learningcited by 13
  14. 2024MEMLA: Enhancing Multilingual Knowledge Editing with Neuron-Masked Low-Rank Adaptationcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Benchmarking Temporal Reasoning and Alignment Across Chinese Dynastiescited by 0
  2. 2026Beyond Local Edits: Embedding-Virtualized Knowledge for Broader Evaluation and Preservation of Model Editingcited by 0
  3. 2026CultureMINE: Datasets and Methods for Improving the Cultural Capabilities of NLP Systemscited by 0