Papers Model Editing
“Model Editing” 태그가 달린 논문 193편 · 필터 해제
Model Editing as a Double-Edged Sword: Steering Agent Ethical Behavior Toward Beneficence or Harm
Agents based on Large Language Models (LLMs) have demonstrated strong capabilities across a wide range of tasks. However, deploying LLM-based agents in high-stakes domains comes with significant safety and ethical risks.…
Model EditingMitigating Safety Fallback in Editing-based Backdoor Injection on LLMs
Large language models (LLMs) have shown strong performance across natural language tasks, but remain vulnerable to backdoor attacks. Recent model editing-based approaches enable efficient backdoor injection by directly m…
DiversityModel EditingSafety AlignmentDualEdit: Dual Editing for Knowledge Updating in Vision-Language Models
Model editing aims to efficiently update a pre-trained model's knowledge without the need for time-consuming full retraining. While existing pioneering editing methods achieve promising results, they primarily focus on e…
Model EditingSoK: Machine Unlearning for Large Language Models
Large language model (LLM) unlearning has become a critical topic in machine learning, aiming to eliminate the influence of specific training data or knowledge without retraining the model from scratch. A variety of tech…
Large Language ModelMachine UnlearningModel EditingMEMOIR: Lifelong Model Editing with Minimal Overwrite and Informed Retention for LLMs
Language models deployed in real-world systems often require post-hoc updates to incorporate new or corrected knowledge. However, editing such models efficiently and reliably - without retraining or forgetting previous i…
HallucinationModel EditingOut-of-Distribution GeneralizationQuestion AnsweringThe OCR Quest for Generalization: Learning to recognize low-resource alphabets with model editing
Achieving robustness in recognition systems across diverse domains is crucial for their practical utility. While ample data availability is usually assumed, low-resource languages, such as ancient manuscripts and non-wes…
Meta-LearningModel EditingOptical Character Recognition (OCR)Transfer LearningDrop Dropout on Single-Epoch Language Model Pretraining
Originally, dropout was seen as a breakthrough regularization technique that reduced overfitting and improved performance in almost all applications of deep learning by reducing overfitting. Yet, single-epoch pretraining…
Language ModelingLanguage ModellingModel EditingNatural Language Inference+1On Fairness of Task Arithmetic: The Role of Task Vectors
Model editing techniques, particularly task arithmetic using task vectors, have shown promise in efficiently modifying pre-trained models through arithmetic operations like task addition and negation. Despite computation…
FairnessHate Speech DetectionModel EditingNegation+2Model Unlearning via Sparse Autoencoder Subspace Guided Projections
Large language models (LLMs) store vast amounts of information, making them powerful yet raising privacy and safety concerns when selective knowledge removal is required. Existing unlearning strategies, ranging from grad…
Adversarial Robustnessfeature selectionGSM8KMMLU+2DocMEdit: Towards Document-Level Model Editing
Model editing aims to correct errors and outdated knowledge in the Large language models (LLMs) with minimal cost. Prior research has proposed a variety of datasets to assess the effectiveness of these model editing meth…
modelModel EditingREACT: Representation Extraction And Controllable Tuning to Overcome Overfitting in LLM Knowledge Editing
Large language model editing methods frequently suffer from overfitting, wherein factual updates can propagate beyond their intended scope, overemphasizing the edited target even when it's contextually inappropriate. To …
knowledge editingLanguage ModelingLanguage ModellingLarge Language Model+1Localizing Knowledge in Diffusion Transformers
Understanding how knowledge is distributed across the layers of generative models is crucial for improving interpretability, controllability, and adaptation. While prior work has explored knowledge localization in UNet-b…
Model EditingDisentangling Knowledge Representations for Large Language Model Editing
Knowledge Editing has emerged as a promising solution for efficiently updating embedded knowledge in large language models (LLMs). While existing approaches demonstrate effectiveness in integrating new knowledge and pres…
Disentanglementknowledge editingLanguage ModelingLanguage Modelling+2Model Editing with Graph-Based External Memory
Large language models (LLMs) have revolutionized natural language processing, yet their practical utility is often limited by persistent issues of hallucinations and outdated parametric knowledge. Although post-training …
graph constructionmodelModel EditingUniErase: Unlearning Token as a Universal Erasure Primitive for Language Models
Large language models require iterative updates to address challenges such as knowledge conflicts and outdated information (e.g., incorrect, private, or illegal contents). Machine unlearning provides a systematic methodo…
Machine UnlearningModel EditingWorld KnowledgeLyapLock: Bounded Knowledge Preservation in Sequential Large Language Model Editing
Large Language Models often contain factually incorrect or outdated knowledge, giving rise to model editing methods for precise knowledge updates. However, current mainstream locate-then-edit approaches exhibit a progres…
Language ModelingLanguage ModellingLarge Language ModelModel EditingEditing Across Languages: A Survey of Multilingual Knowledge Editing
While Knowledge Editing has been extensively studied in monolingual settings, it remains underexplored in multilingual contexts. This survey systematizes recent research on Multilingual Knowledge Editing (MKE), a growing…
knowledge editingModel EditingSurveyUltraEdit: Training-, Subject-, and Memory-Free Lifelong Editing in Large Language Models
Lifelong learning enables large language models (LLMs) to adapt to evolving information by continually updating their internal knowledge. An ideal system should support efficient, wide-ranging updates while preserving ex…
GPULifelong learningModel EditingUniEdit: A Unified Knowledge Editing Benchmark for Large Language Models
Model editing aims to enhance the accuracy and reliability of large language models (LLMs) by efficiently adjusting their internal parameters. Currently, most LLM editing datasets are confined to narrow knowledge domains…
Diversityknowledge editingKnowledge GraphsModel EditingCross-Model Transfer of Task Vectors via Few-Shot Orthogonal Alignment
Task arithmetic enables efficient model editing by representing task-specific changes as vectors in parameter space. Task arithmetic typically assumes that the source and target models are initialized from the same pre-t…
Model EditingTask Arithmetic