--- title: "Who cites In-Context Unlearning: Language — 68 citing papers" description: "68 papers of the Elamur Research library cite «In-Context Unlearning: Language Models as Few Shot Unlearners», found in their…" canonical: "https://research.elamur.ai/free/en/papers/pawelczyk-2023-context-unlearning-language-models-few/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/pawelczyk-2023-context-unlearning-language-models-few/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [In-Context Unlearning: Language Models as Few…](https://md.research.elamur.ai/free/en/papers/pawelczyk-2023-context-unlearning-language-models-few/) / cited by # Who cites «In-Context Unlearning: Language Models as Few Shot Unlearners» 68 papers of our library cite it (of the 149,439 whose bibliographies we read) · 18 with a page here, the most cited first · [← back to «In-Context Unlearning: Language Models as Few Shot Unlearners»](https://md.research.elamur.ai/free/en/papers/pawelczyk-2023-context-unlearning-language-models-few/) 1. **[Large Language Model Unlearning](https://md.research.elamur.ai/free/en/papers/yao-2023-large-language-model-unlearning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuanshun Yao, Xiaojun Xu, Yang Liu*2023 · 174 citations* 2. [**TOFU: A Task of Fictitious Unlearning for LLMs arXiv:2401.06121**Pratyush Maini, Zhili Feng, Avi Schwarzschild, et al.*2024 · 144 citations*](https://md.research.elamur.ai/free/en/papers/maini-2024-tofu-task-fictitious-unlearning-llms/) 3. [**The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning arXiv:2403.03218**Nathaniel Li, Alexander Pan, Anjali Gopal, et al.*2024 · 127 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-wmdp-benchmark-measuring-reducing-malicious/) 4. [**Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning arXiv:2404.05868**Ruiqi Zhang, Licong Lin, Yu Bai, et al.*2024 · 126 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-negative-preference-optimization-catastrophic-collapse/) 5. [**From Persona to Personalization: A Survey on Role-Playing Language Agents arXiv:2404.18231**Jiangjie Chen, Xintao Wang, Rui Xu, et al.*2024 · 115 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-persona-personalization-survey-role-playing/) 6. [**Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves arXiv:2311.04205**Yihe Deng, Weitong Zhang, Zixiang Chen, et al.*2023 · 62 citations*](https://md.research.elamur.ai/free/en/papers/deng-2023-rephrase-respond-let-large-language/) 7. **[SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning](https://md.research.elamur.ai/free/en/papers/jia-2024-soul-unlocking-power-second-order/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jinghan Jia, Yihua Zhang, Yimeng Zhang, et al.*2024 · 46 citations* 8. [**MoRAL: MoE Augmented LoRA for LLMs' Lifelong Learning arXiv:2402.11260**Shu Yang; Muhammad Asif Ali; Cheng-Long Wang; Lijie Hu;, Di Wang*2024 · 21 citations*](https://md.research.elamur.ai/free/en/papers/yang-2024-moral-moe-augmented-lora-llms/) 9. **[Soft Prompting for Unlearning in Large Language Models](https://md.research.elamur.ai/free/en/papers/bhaila-2025-soft-prompting-unlearning-large-language/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Karuna Bhaila, Minh-Hao Van, Xintao Wu*2025 · 16 citations* 10. [**RKLD: Reverse KL-Divergence-based Knowledge Distillation for Unlearning Personal Information in Large Language Models arXiv:2406.01983**Bichen Wang, Yuzhe Zi, Yixin Sun, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-rkld-reverse-kl-divergence-based/) 11. **[Model Unlearning via Sparse Autoencoder Subspace Guided Projections](https://md.research.elamur.ai/free/en/papers/wang-2025-model-unlearning-sparse-autoencoder-subspace/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xu Wang, Zihao Li, Benyou Wang, et al.*2025 · 8 citations* 12. **[Modeling LLM Unlearning as an Asymmetric Two-Task Learning Problem](https://md.research.elamur.ai/free/en/papers/xiao-2026-modeling-llm-unlearning-as-asymmetric/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zeguan Xiao, Siqing Li, Yong Wang, et al.*2026 · 2 citations* 13. **[Erasing Without Remembering: Implicit Knowledge Forgetting in Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-erasing-without-remembering-implicit-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Huazheng Wang, Yongcheng Jing, Haifeng Sun, et al.*2026 · 1 citation* 14. **[CAP: Controllable Alignment Prompting for Unlearning in LLMs](https://md.research.elamur.ai/free/en/papers/wang-2026-cap-controllable-alignment-prompting-unlearning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhaokun Wang, Jinyu Guo, Jingwen Pu, et al.*2026* 15. **[CheckMIABench: Firm Foundations For Membership Inference Attacks on Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-checkmiabench-firm-foundations-membership-inference/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jeffrey George Wang, Jason Wang, Marvin Li, et al.*2026* 16. **[Decoding-Unlearning: Fact Forgetting via Entropy-Guided Inference](https://md.research.elamur.ai/free/en/papers/pu-2026-decoding-unlearning-fact-forgetting-entropy/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jingwen Pu, Mingjun Shi, Xinrui Ren, et al.*2026* 17. **[From Domains to Instances: Dual-Granularity Data Synthesis for LLM Unlearning](https://md.research.elamur.ai/free/en/papers/xu-2026-domains-instances-dual-granularity-data/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaoyu Xu, Minxin Du, Zitong LI, et al.*2026* 18. **[LOKA: Conflict-Aware LLM Knowledge Update with Adaptive Knowledge Memory](https://md.research.elamur.ai/free/en/papers/zhang-2026-loka-conflict-aware-llm-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Binchi Zhang, Zhengzhang Chen, Zaiyi Zheng, et al.*2026* 50 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/pawelczyk-2023-context-unlearning-language-models-few/) — In-Context Unlearning: Language Models as Few Shot Unlearners