Papers with IKE
Persuasion Tokens for Editing Factual Knowledge in LLMs (2026.eacl-short)
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| Challenge: | In-context knowledge editing (IKE) relies on fact-specific demonstrations which consume significant context window space. |
| Approach: | They introduce persuasion tokens (P-Tokens) which replicate the effect of IKE demonstrations and allow efficient knowledge editing without requiring fact-specific demonstrations. |
| Outcome: | The proposed tokens perform comparable to and often exceed IKE on two editing datasets and three LLMs and increase the number of tokens increases performance. |
COMEM: In-Context Retrieval-Augmented Mass-Editing Memory in Large Language Models (2024.findings-naacl)
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| Challenge: | Existing methods for "knowledge editing" in large language models are inadequate . authors propose a method that can be used to update outdated information or correct false information . |
| Approach: | They propose a unified knowledge editing method called in-COntext retrieval-augmented Mass-Editing Memory . it incorporates retrieval augmented IKE, a novel extension of IKE designed for massive editing tasks . |
| Outcome: | The proposed method outperforms existing methods on the zsRE and CounterFact datasets. |
From Text to Emoji: How PEFT-Driven Personality Manipulation Unleashes the Emoji Potential in LLMs (2025.findings-naacl)
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Navya Jain, Zekun Wu, Cristian Enrique Munoz Villalobos, Airlie Hilliard, Xin Guan, Adriano Koshiyama, Emre Kazim, Philip Colin Treleaven
| Challenge: | Methods like prompt-based In-Context Knowledge Editing and gradient-based Model Editor Networks (MEND) show irregularity and variability; IKE depends on the prompt, leading to variability and sensitivity; MEND yields inconsistent and gibberish outputs. |
| Approach: | They employ Opinion QA Based Parameter-Efficient Fine-Tuning (PEFT) to manipulate the Big Five personality traits: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. |
| Outcome: | The proposed methods show that they are more accurate than prompt-based IKE and gradient-based MEND outputs. |
Editing the Moving World: Model Editing for Video LLMs (2026.acl-long)
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Qian Zhang, Xinye Li, Xiaokai Wu, Junhao Xu, Zhanyue Qin, Qingbin Liu, Junxian Cai, Xi Chen, Bolin Zhang, Zhiying Tu, Dianhui Chu, Xiaoyan Yu, Dianbo Sui
| Challenge: | Existing models for knowledge editing focus on knowledge-level or static visual domains, overlooking dynamic semantics. |
| Approach: | They propose a benchmark for modeling large language models using six representative models . they analyze the strengths and limitations of existing models and identify new directions . |
| Outcome: | The proposed benchmark extends existing models from static modalities to dynamic video scenarios. |
Can We Edit Factual Knowledge by In-Context Learning? (2023.emnlp-main)
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| Challenge: | In-context knowledge editing (IKE) is a new paradigm for NLP research that can be applied to large language models with tens or hundreds of parameters. |
| Approach: | They propose to use in-context knowledge editing (IKE) without gradient updating to edit factual knowledge without a gradient update. |
| Outcome: | The proposed method achieves a competitive success rate compared to gradient-based methods on GPT-J but with fewer side effects. |
DistillMIKE: Editing Distillation of Massive In-Context Knowledge Editing in Large Language Models (2024.findings-acl)
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| Challenge: | In-context knowledge editing has shown respectable abilities on knowledge editing in terms of generalization and specificity. |
| Approach: | They propose a novel extension of in-context knowledge editing (IKE) that allows for massive edits to be injected into large language models. |
| Outcome: | The proposed method shows state-of-the-art perfomrances and comparable performance with MIKE. |
How to Make LLMs Forget: On Reversing In-Context Knowledge Edits (2025.naacl-long)
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| Challenge: | In-context knowledge editing (IKE) is an efficient and efficient knowledge editing method (Zheng et al., 2022b; Gangadhar and Stratos, 2024) it can be misused to manipulate responses opaquely, e.g., insert misinformation or offensive content. |
| Approach: | They propose to detect and reverse IKE-edits using only the top-10 output probabilities of the next token, even in a black-box setting. |
| Outcome: | The proposed method can be detected with high accuracy even in a black-box setting, achieving over 80% accuracy in recovering original, unedited outputs across multiple LLMs. |
LLMs Meet Isolation Kernel: Lightweight, Learning-free Binary Embeddings for Fast Retrieval (2026.findings-acl)
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| Challenge: | Large language models (LLMs) embeddings are typically high-dimensional, leading to substantial storage and retrieval overhead. |
| Approach: | They propose a learning-free method that transforms an LLM embedding into a binary embeddable using Isolation Kernel (IKE). |
| Outcome: | The proposed method performs 16.7 faster retrieval and 16 lower memory usage than the original LLM embeddings while maintaining comparable accuracy. |
BMIKE-53: Investigating Cross-Lingual Knowledge Editing with In-Context Learning (2025.acl-long)
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| Challenge: | Using a benchmark for cross-lingual knowledge editing, knowledge editing is underexplored. |
| Approach: | They propose a benchmark for cross-lingual in-context knowledge editing that spans 53 languages and three KE datasets. |
| Outcome: | The proposed benchmark systematically evaluates cross-lingual knowledge editing (IKE) under zero-shot, one-shot and few-shot setups. |
Confidence-Aware Ranker Ensembles for Robust In-Context Knowledge Editing (2026.findings-acl)
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| Challenge: | Large language models excel at factual recall, but can propagate stale or incorrect knowledge. |
| Approach: | They propose a feature-weighted ensemble for in-context knowledge editing that calibrates three heterogeneous rankers and extracts simple confidence features from each ranker. |
| Outcome: | The proposed method achieves 88.33% Edit-Success Rate over the best single retriever . it improves edit accuracy without touching model weights and approaches oracle upper bound (91%). |