Can We Edit Factual Knowledge by In-Context Learning?
Open accessAbstract
Previous studies have shown that large language models (LLMs) like GPTs store massive factual knowledge in their parameters.However, the stored knowledge could be false or outdated.Traditional knowledge editing methods refine LLMs via fine-tuning on texts containing specific knowledge.However, with the increasing scales of LLMs, these gradient-based approaches bring large computation costs.The trend of model-as-a-service also makes it impossible to modify knowledge in black-box LLMs.Inspired by in-context learning (ICL), a new paradigm based on demonstration contexts without parameter updating, we explore whether ICL can edit factual knowledge.To answer this question, we give a comprehensive empirical study of ICL strategies.Experiments show that in-context knowledge editing (IKE), without any gradient and parameter updating, achieves a competitive success rate compared to gradient-based methods on GPT-J (6B) but with much fewer side effects, including less over-editing on similar but unrelated facts and less knowledge forgetting on previously stored knowledge.We also apply the method to larger LMs with tens or hundreds of parameters like OPT-175B, which shows the scalability of our method.The code is available at https://github.com/pkunlp-icler/IKE.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Memory-based approaches require training to update memory representations or routing, and memory consumption grows linearly with the number of edits, limiting practicality in lifelong model editing.
“However, these methods still require training to update memory representations or routing, and inference must be rerouted through the memory. In addition, they maintain one entry per edit, causing memory consumption to grow linearly with the number of edits, which limits their practicality in lifelong model editing with high-frequency or large-scale updates.”
From UltraEdit: Training-, Subject-, and Memory-Free Lifelong Editing in Language Models (2025) · cites this paper as Zheng et al., 2023
Section E.1 (page unavailable) describes a memory-based method that stores multiple factual edits externally and uses a scope classifier to retrieve a relevant edit for a prompt; Section 2 also describes an edit-memory retriever. The copy does not say that memory representations or routing must be trained, that storage grows linearly with edits, or that this limits lifelong editing. (LLM, conf=0.83)
Experimental — accuracy not yet measuredIn-context learning ability enhances models' understanding of updated knowledge.
“Although in-context learning ability enhances models' understanding of updated knowledge (Zheng et al., 2023)”
From Reason-KE++: Aligning the Process, Not Just the Outcome, for Faithful LLM Knowledge Editing (2025) · cites this paper as Zheng et al., 2023
The Main Results and Conclusion (sections 5.2 and 7; page numbers unavailable) report that in-context knowledge editing improves models’ use and generalization of updated facts. They do not measure models’ understanding or establish that stronger in-context learning ability itself causes that improvement. (LLM, conf=0.78)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Reimers, N., & Gurevych, I. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1410 · Cited by 12,778 · see citing works
(2017) · Cited by 11,625 · see citing works
LLaMA: Open and Efficient Foundation Language Models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.13971 · Cited by 4,602 · see citing works
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208 · see citing works
HuggingFace's Transformers: State-of-the-art Natural Language Processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2019) · arXiv · doi:10.48550/arxiv.1910.03771 · Cited by 3,347 · see citing works
Llama 2: Open Foundation and Fine-Tuned Chat Models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2307.09288 · Cited by 2,604 · see citing works
Evaluating Large Language Models Trained on Code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. D. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al. (2021) · arXiv · doi:10.48550/arxiv.2107.03374 · Cited by 1,436 · see citing works
FEVER: a Large-scale Dataset for Fact Extraction and VERification
Thorne, J., Vlachos, A., Christodoulopoulos, C., & Mittal, A. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1074 · Cited by 1,343 · see citing works
Finetuned Language Models Are Zero-Shot Learners
Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., & Le, Q. V. (2021) · arXiv · doi:10.48550/arxiv.2109.01652 · Cited by 1,101 · see citing works
RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models
Gehman, S., Gururangan, S., Sap, M., Choi, Y., & Smith, N. A. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.301 · Cited by 693 · see citing works
Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity
Lu, Y., Bartolo, M., Moore, A., Riedel, S., & Stenetorp, P. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.556 · Cited by 610 · see citing works
What Makes Good In-Context Examples for GPT-3?
Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., & Chen, W. (2022) · Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures · doi:10.18653/v1/2022.deelio-1.10 · Cited by 563 · see citing works
Calibrate Before Use: Improving Few-Shot Performance of Language Models
Zhao, T. Z., Wallace, E., Feng, S., Klein, D., & Singh, S. (2021) · arXiv · doi:10.48550/arxiv.2102.09690 · Cited by 513 · see citing works
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
Gao, L., Biderman, S., Black, S., Golding, L., Hoppe, T., Foster, C., Phang, J., He, H., Thite, A., Nabeshima, N., et al. (2021) · arXiv · doi:10.48550/arxiv.2101.00027 · Cited by 492 · see citing works
Zero-Shot Relation Extraction via Reading Comprehension
Levy, O., Seo, M., Choi, E., & Zettlemoyer, L. (2017) · arXiv · doi:10.48550/arxiv.1706.04115 · Cited by 482
Zero-Shot Relation Extraction via Reading Comprehension
Levy, O., Seo, M., Choi, E., & Zettlemoyer, L. (2017) · Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017) · doi:10.18653/v1/k17-1034 · Cited by 482 · see citing works
The Woman Worked as a Babysitter: On Biases in Language Generation
Sheng, E., Chang, K.-W., Natarajan, P., & Peng, N. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1339 · Cited by 431 · see citing works
Prompting GPT-3 To Be ReliablePreprint
Si, C., Gan, Z., Yang, Z.-Y., Wang, S.-H., Wang, J., Boyd-Graber, J. L., & Wang, L.-J. (2022) · International Conference on Learning Representations · doi:10.48550/arxiv.2210.09150 · Cited by 395 · see citing works
Learning To Retrieve Prompts for In-Context Learning
Rubin, O., Herzig, J., & Berant, J. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.191 · Cited by 374 · see citing works
WebGPT: Browser-assisted question-answering with human feedback
Nakano, R., Hilton, J., Balaji, S., Wu, J., Ouyang, L., Kim, C., Hesse, C., Jain, S., Kosaraju, V., Saunders, W., et al. (2021) · arXiv · doi:10.48550/arxiv.2112.09332 · Cited by 272 · see citing works
A Survey on In-context Learning
Dong, Q., Li, L., Dai, D., Zheng, C., Ma, J., Li, R., Xia, H., Xu, J., Wu, Z., Liu, T., et al. (2023) · arXiv · doi:10.48550/arxiv.2301.00234 · Cited by 248 · see citing works
Self-citationLocating and Editing Factual Associations in GPT
Meng, K., Bau, D., Andonian, A., & Belinkov, Y. (2022) · arXiv · doi:10.48550/arxiv.2202.05262 · Cited by 230 · see citing works
Measuring and improving consistency in pretrained language models
Elazar, Y., Kassner, N., Ravfogel, S., Ravichander, A., Hovy, E., Schütze, H., & Goldberg, Y. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00410 · Cited by 220 · see citing works
Knowledge Neurons in Pretrained Transformers
Dai, D., Dong, L., Hao, Y., Sui, Z., Chang, B., & Wei, F. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.581 · Cited by 187 · see citing works
Self-citationEditing Factual Knowledge in Language Models
De Cao, N., Aziz, W., & Titov, I. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.522 · Cited by 180 · see citing works
Time-Aware Language Models as Temporal Knowledge Bases
Dhingra, B., Cole, J. R., Eisenschlos, J. M., Gillick, D., Eisenstein, J., & Cohen, W. W. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00459 · Cited by 169 · see citing works
(2023) · doi:10.18653/v1/2023.emnlp-main.632 · Cited by 115 · see citing works
Black-Box Tuning for Language-Model-as-a-Service
Sun, T., Shao, Y., Qian, H., Huang, X., & Qiu, X. (2022) · arXiv · doi:10.48550/arxiv.2201.03514 · Cited by 100 · see citing works
Mitchell, E., Lin, C., Bosselut, A., Finn, C., & Manning, C. D. (2021) · arXiv · doi:10.48550/arxiv.2110.11309 · Cited by 95 · see citing works
Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases
Cao, B., Lin, H., Han, X., Sun, L., Yan, L., Liao, M., Xue, T., & Xu, J. (2021) · doi:10.18653/v1/2021.acl-long.146 · Cited by 90 · see citing works
Self-citationMass-Editing Memory in a Transformer
Meng, K., Sharma, A. S., Andonian, A., Belinkov, Y., & Bau, D. (2022) · arXiv · doi:10.48550/arxiv.2210.07229 · Cited by 76 · see citing works
GPT-NeoX-20B: An Open-Source Autoregressive Language Model
Black, S., Biderman, S., Hallahan, E., Anthony, Q., Gao, L., Golding, L., He, H., Leahy, C., McDonell, K., Phang, J., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.06745 · Cited by 67 · see citing works
Memory-Based Model Editing at Scale
Mitchell, E., Lin, C., Bosselut, A., Manning, C. D., & Finn, C. (2022) · arXiv · doi:10.48550/arxiv.2206.06520 · Cited by 51 · see citing works
Human-Level Play in the Game of Diplomacy by Combining Language Models with Strategic Reasoning
Bakhtin, A., Brown, N., Dinan, E., Farina, G., Flaherty, C., Fried, D., Goff, A., Gray, J., Hu, H., Jacob, A. P., et al. (2022) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.7236699 · Cited by 35 · see citing works
Calibrating Factual Knowledge in Pretrained Language ModelsPreprint
Dong, Q., Dai, D., Song, Y., Xu, J., Sui, Z., & Li, L. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2210.03329 · Cited by 4 · see citing works
Self-citationOPT: Open Pre-trained Transformer Language Models
Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M., Li, X., Lin, X. V., et al. (2022) · arXiv · doi:10.48550/arxiv.2205.01068
Flagged first, then by citations
Research works printed but not linked: 3
- PMLR.
- Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9.
- Yunzhi Yao, Peng Wang, Bo Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, and Ningyu Zhang. 2023. Editing large language mod- els: Problems, methods, and opportunities. ArXiv, abs/2305.13172. · arXiv
Cited by
Sorted by citations
Towards trustworthy LLMs: a review on debiasing and dehallucinating in large language models
Lin, Z., Guan, S., Zhang, W., Zhang, H., Li, Y., & Zhang, H. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10896-y · Cited by 141 · see citing works
(2023) · doi:10.18653/v1/2023.emnlp-main.632 · Cited by 115 · see citing works
Rethinking machine unlearning for large language models
Liu, S., Yao, Y., Jia, J., Casper, S., Baracaldo, N., Hase, P., Yao, Y., Liu, C. Y., Xu, X., Li, H., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-00985-0 · Cited by 173 · see citing works
Next-Generation Database Interfaces: A Survey of LLM-Based Text-to-SQL
Hong, Z., Yuan, Z., Zhang, Q., Chen, H., Dong, J., Huang, F., & Huang, X. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3609486 · Cited by 181 · see citing works
Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-Based Retrofitting
Guan, X., Liu, Y., Lin, H., Lu, Y., He, B., Han, X., & Sun, L. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i16.29770 · Cited by 101 · see citing works
Knowledge Editing for Large Language Models: A Survey
Wang, S., Zhu, Y., Liu, H., Zheng, Z., Chen, C., & Li, J. (2025) · ACM Computing Surveys · doi:10.1145/3698590 · Cited by 79 · see citing works
PMET: Precise Model Editing in a Transformer
Li, X., Li, S., Song, S., Yang, J., Ma, J., & Yu, J. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29818 · Cited by 45 · see citing works
Evaluating the Ripple Effects of Knowledge Editing in Language Models
Cohen, R., Biran, E., Yoran, O., Globerson, A., & Geva, M. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00644 · Cited by 72 · see citing works
How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances
Zhang, Z., Fang, M., Chen, L., Namazi‐Rad, M., & Wang, Y.-X. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.516 · Cited by 37 · see citing works
Can We Edit Multimodal Large Language Models?
Cheng, S., Tian, B., Liu, Q., Chen, X., Wang, Y., Chen, H., & Zhang, N. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.856 · Cited by 33 · see citing works
Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models
Xu, D., Zhang, Z., Zhu, Z., Lin, Z., Liu, Q., Wu, X., Xu, T., Wang, W., Ye, Y., Zhao, X., et al. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679673 · Cited by 20 · see citing works
Conformity in large language models
Zhu, X., Zhang, C., Stafford, T., Collier, N., & Vlachos, A. (2025) · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2025.acl-long.195 · Cited by 10 · see citing works
Editing Personality For Large Language Models
Mao, S., Wang, X., Wang, M., Jiang, Y., Xie, P., Huang, F., & Zhang, N. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-9434-8_19 · Cited by 9 · see citing works
History Matters: Temporal Knowledge Editing in Large Language Model
Yin, X., Jin, J., Yang, L., & Wan, X. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29912 · Cited by 14 · see citing works
In-Context Learning in Large Language Models: A Comprehensive SurveyPreprint
Highmore, C. (2024) · Preprints.org · doi:10.20944/preprints202407.0926.v1 · Cited by 8 · see citing works
DocTER: Evaluating document-based knowledge editing
Wu, S., Wang, A., Peng, M., Lin, Y., Li, W., Sun, M., & Su, J. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104299 · Cited by 17 · see citing works
Evaluating Complex Entity Knowledge Propagation for Knowledge Editing in LLMs
Shafqat, W., & Na, S. (2024) · Applied Sciences · doi:10.3390/app14041508 · Cited by 5 · see citing works
WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models
Wang, P., Li, Z., Zhang, N., Xu, Z., Yao, Y., Jiang, Y., Xie, P., Huang, F., & Chen, H. (2024) · arXiv · doi:10.48550/arxiv.2405.14768 · Cited by 19 · see citing works
Liu, S., Xie, H., Lei, Y., Zhao, J., & Song, X. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.131343 · Cited by 4 · see citing works
Chen, Y., Rao, J., Zeng, Y., Zha, M., Yan, W., Jiang, Z., & Liu, Y. (2026) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2026.03.007 · Cited by 4 · see citing works
Atomic Fact Decomposition Helps Attributed Question Answering
Yan, Z., Wang, J., Chen, J., Li, X., Liang, J., Li, R., & Pan, J. Z. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3608716 · Cited by 6 · see citing works
Knowledge Editing through Chain-of-Thought
Wang, C., Su, W., Ai, Q., Tang, Y., & Liu, Y. (2025) · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2025.emnlp-main.540 · Cited by 3 · see citing works
FiNE: Fine-Grained Neuron-Level Model Editing for Reliable and Safe LLMs
Yang, X., Pan, H., Wang, X., Cao, Y., Li, J., & Wang, M. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3697976 · Cited by 2 · see citing works
Wei, Y., Yu, X., Weng, Y., Ma, H., Zhang, Y., Zhao, J., & Liu, K. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679900 · Cited by 2 · see citing works
Can We Debias Multimodal Large Language Models via Model Editing?
Wang, Z., Li, X., Qin, Z., Li, C., Tu, Z., Chu, D., & Sui, D. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681589 · Cited by 2 · see citing works
LLM+KG@VLDB 24 Workshop Summary
Khan, A., Wu, T., & Chen, X. (2025) · ACM SIGMOD Record · doi:10.1145/3749116.3749132 · Cited by 2 · see citing works
Embracing Collaboration Over Competition: Condensing Multiple Prompts for Visual In-Context Learning
Wang, J., Luo, T., Zha, Y., Feng, Y., Luo, R., Chen, B., Dai, T., Chen, L., Wang, Y., & Xia, S. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02342 · Cited by 1 · see citing works
Leveraging Graph Transformers for Knowledge Editing in Large Language Models
Gao, W. G. W., Li, X. Y., Cao, Y., Zhang, G. W. J. J. W., Yang, W., & Hu, G. (2026) · Human-Centric Intelligent Systems · doi:10.1007/s44230-026-00136-7 · Cited by 1 · see citing works
Editing Knowledge Representation of Language Model via Rephrased Prefix Prompts
Cai, Y., Cao, D., Guo, R., Wen, Y., Liu, G., Chen, E., & Zhang, J. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5672-8_39 · Cited by 1 · see citing works
Assessing and Post-Processing Black Box Large Language Models for Knowledge Editing
Song, X., Wang, Z., He, K., Dong, G., Mou, Y., Zhao, J., & Xu, W. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3696410.3714732 · Cited by 1 · see citing works
UltraEdit: Training-, Subject-, and Memory-Free Lifelong Editing in Language Models
Gu, X., Huang, Z., Gu, J.-C., & Zhang, K. (2025) · arXiv · doi:10.48550/arxiv.2505.14679
Zhang, N., Yao, Y., & Deng, S. (2023) · doi:10.18653/v1/2023.ijcnlp-tutorials.4
Learning from Mistakes: A Comprehensive Review of Knowledge Editing for Large Language Models
Li, Y., Fan, C., Huang, M., & Li, C. (2024) · IEEE International Conference on Smart Internet of Things (SmartIoT) · doi:10.1109/smartiot62235.2024.00092
Multi-MELO: Unified multimodal model editing with dynamic LoRA
Chen, Q., Yin, J., Yu, L., Zhou, J., & He, L. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126766
A Two-Stage Approach for Knowledge Editing in LLM
Xiong, H., Shao, W., Han, T., & Chen, W. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-1809-5_31
Harnessing In-Context Learning for Personality Elicitation in Large Language Models
Zhang, H. (2024) · IEEE International Conference on Frontiers Technology of Information and Computer (ICFTIC) · doi:10.1109/icftic64248.2024.10913132
MECA: Modular editing via customized expert networks and adaptors in large language models
Nyange, R., Qiao, S., & Na, S. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129675
ADS-Edit: A Multimodal Knowledge Editing Dataset for Autonomous Driving Systems
Wang, C., Fang, J., Chen, X., Tian, B., Xu, Z., Chen, H., & Zhang, N. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3758237
ReFEdit: Rehearsal-Free Lifelong Knowledge Editing for Large Language Models
Mo, X., Pan, Y.-C., Hou, Y., Luo, P., & Xiang, Y. (2025) · IEEE International Conference on Multimedia and Expo · doi:10.1109/icme59968.2025.11209132
MPN: Leveraging Multilingual Patch Neuron for Cross-Lingual Model Editing
Si, N., Chang, H., Zhang, W.-Q., & Zhang, W. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3052-6_10
Towards Benign Memory Forgetting for Selective Multimodal Large Language Model Unlearning
Zeng, Z., Gu, L., Duan, Z., Li, F., Snoek, C. G. M., Wang, M., & Shi, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37447-9_29
A Fine-Grained Model Editing Method Based on the Importance of Parameters
Wang, J., Song, C., & Ge, T. (2026) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2026.3652007
Teacher-Guided Code Generation With API Update
Sun, T., Wang, Z., Wang, W., Chen, R., & Pei, J. (2025) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc58881.2025.11342663
MAKE: Memory-Associated Knowledge Editing
Park, S., Park, S., Kim, J., & Kim, H. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.26
Bidirectional Multi-Hop Expansion Challenges in Knowledge Editing for Large Language Models
Wang, Z., Wang, H., Wang, C., & Xu, W.-R. (2025) · International Conference on Computer Science and Artificial Intelligence · doi:10.1145/3788149.3788216
Prompt-based Relation Extraction for Pharmacovigilance
Yeh, H.-S. (2024) · theses.fr (ABES) · doi:10.70675/762a3598ze739z4c47za72ez62e5a986fd1f
Mitigating Deceptive Knowledge Editing in LLMS via Diffusion Synthesis
Zhang, H., Hu, H., Cheng, G., You, H., Gong, Y., & Huang, J. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11461953
Knowledge Editing with Demonstration Selection for Multi-Hop Question Answering
Zhao, H., Li, J., Tan, C., Yılmaz, E., & Liang, S. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11462495
Hu, M., Song, D., Liu, X., Sun, X., & Zhang, Y. (2026) · International Journal of Data Science and Analytics · doi:10.1007/s41060-026-01110-z
Fang, L., Wang, X., Wang, D., Wu, Z., Guo, Y., Zhu, H., Zhang, Z., & Liu, G. H. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00239
Self-citationKEditVis: A Visual Analytics System for Knowledge Editing of Large Language Models
Chen, Z., Zhan, H., Huang, Y., Wu, X., Deng, D., Weng, D., & Wu, Y. (2026) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2026.3694436
Beyond Over-Editing: Important Weight Constrained Knowledge Editing in Large Language Models
Zhu, Z. G., Dai, Y., Guo, B., Zheng, H., Sun, Y., & Fan, Z. (2026) · International Conference on Multimedia Retrieval · doi:10.1145/3805622.3810656
Merging Methods for Multilingual Knowledge Editing for Large Language Models: An Empirical Odyssey
Lee, K., Shin, K.-Y., Lee, J.-H., & Suh, Y.-J. (2026) · Electronics · doi:10.3390/electronics15122747
NeuCon-ICE: Neuron-Level Controllable In-Context Editing for Multimodal Large Language Models
Jiang, C., Liao, J., & Zhao, X. H. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3809592
KMAP: Knowledge-Controlled Multi-agentic LLM Peer Learning
Nakazono, K., & Ma, Q. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-34374-1_16
CREME: Robustness Enhancement of Code LLMs via Layer-Aware Model Editing
Liu, S.-S., Hu, X., Huang, K., Yang, X., Lo, D., & Xia, X. (2026) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3744916.3773111 · Cited by 1 · see citing works
Leveraging Cross-Modal Knowledge Transfer for Knowledge-Aware Concept Customization
Zhu, C., Li, H., Li, X., & Chen, L. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37531-5_33 · Cited by 1 · see citing works
Wiangnak, P., Kertkeidkachorn, N., & Shirai, K. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-42026-8_18
Reason-KE++: Aligning the Process, Not Just the Outcome, for Faithful LLM Knowledge Editing
Wu, Y., Ding, L., Shen, L., & Tao, D. (2025) · arXiv · doi:10.48550/arxiv.2511.12661
- Authors
- Zheng, C., Li, L., Dong, Q., Fan, Y., Wu, Z., Xu, J., & Chang, B.
- Affiliations
- Peking University (CN)
- Shanghai Artificial Intelligence Laboratory (CN)
- Venue
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Published
- 2023
- Pages
- 4862-4876
- OpenAlex
- W4389518797
- Cited by
- 54
- References
- 36
- Citations by year
- 2026: 17 · 2025: 17 · 2024: 16 · 2023: 4
- FWCI
- 9.57
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61936012)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- in-context learning, large language models, knowledge forgetting, GPT-J
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
