knowledge editing
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Benchmarks
zsRE
Most implemented
Cross-Lingual Knowledge Editing in Large Language Models
Retrieval-augmented Multilingual Knowledge Editing
Neuron-Level Knowledge Attribution in Large Language Models
Benchmarking and Rethinking Knowledge Editing for Large Language Models
AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models
In-Context Editing: Learning Knowledge from Self-Induced Distributions
Papers
KLOD: Locality-Preserving Knowledge Editing via Non-Target Distribution Preservation
Fine-tuning-based knowledge editing is simple and architecture-agnostic, but standard cross-entropy increases the edited target probability without explicitly constraining changes in the non-target output distribution. I…
knowledge editingPersonaEdit: Representative Sample Selection for Personalized Model Editing
Personalization has attracted growing interest in LLM applications, yet existing retrieval-based approaches depend heavily on retrieval quality and degrade in long-term interactions. Model editing, which directly modifie…
knowledge editingLeveraging Association Context Retrieval in Knowledge Edit- ing to Build White-Box Attacks on LLMs
As large language models (LLMs) are granted increasing autonomy, it is essential to investigate methods that can induce unsafe behavior. We propose a novel white-box attack inspired by locate-then-edit approaches from th…
knowledge editingHybrid-Policy Self-Editing for Composable Unstructured Knowledge Editing
Large language models (LLMs) achieve remarkable performance across natural language tasks, yet they are trained on static corpora and their knowledge quickly becomes outdated in a fast-changing world. This motivates know…
knowledge editingForgetBench: Benchmarking Forgetting Dynamics of Long-Term Parametric Memory in Language Models
Large language models (LLMs) have demonstrated strong capabilities in knowledge acquisition and reasoning, yet their ability to retain previously acquired knowledge under repeated updates remains insufficiently understoo…
knowledge editingWeight-Adjusted Gradients Reveal Parameter Importance and Failure Modes in LLMs
Understanding which parameters are influential in Large Language Models (LLMs) is central to improving their efficiency, reliability, and interpretability. We introduce Weight-Adjusted Gradients (WAG), a simple yet effec…
knowledge editing