Model Editing
49 papers with code • 0 benchmarks • 1 datasets
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Libraries
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KnowEdit
Most implemented papers
Fast Model Editing at Scale
eric-mitchell/mend
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ICLR 2022
To enable easy post-hoc editing at scale, we propose Model Editor Networks using Gradient Decomposition (MEND), a collection of small auxiliary editing networks that use a single desired input-output pair to make fast, local edits to a pre-trained model's behavior.
Editing Large Language Models: Problems, Methods, and Opportunities
zjunlp/easyedit
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Our objective is to provide valuable insights into the effectiveness and feasibility of each editing technique, thereby assisting the community in making informed decisions on the selection of the most appropriate method for a specific task or context.
pyvene: A Library for Understanding and Improving PyTorch Models via Interventions
Interventions on model-internal states are fundamental operations in many areas of AI, including model editing, steering, robustness, and interpretability.
Locating and Editing Factual Associations in GPT
kmeng01/rome
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To test our hypothesis that these computations correspond to factual association recall, we modify feed-forward weights to update specific factual associations using Rank-One Model Editing (ROME).
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
Machine learning (ML) interpretability techniques can reveal undesirable patterns in data that models exploit to make predictions--potentially causing harms once deployed.
A Comprehensive Study of Knowledge Editing for Large Language Models
zjunlp/easyedit
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In this paper, we first define the knowledge editing problem and then provide a comprehensive review of cutting-edge approaches.
A Unified Framework for Model Editing
scalable-model-editing/unified-model-editing
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We introduce a unifying framework that brings two leading "locate-and-edit" model editing techniques -- ROME and MEMIT -- under a single conceptual umbrella, optimizing for the same goal, which we call the preservation-memorization objective.
ModelPS: An Interactive and Collaborative Platform for Editing Pre-trained Models at Scale
AI engineering has emerged as a crucial discipline to democratize deep neural network (DNN) models among software developers with a diverse background.
Learning to Model Editing Processes
We introduce baseline results and metrics on this task, finding that modeling editing processes improves performance on a variety of axes on both our proposed task and related downstream tasks compared to previous single-step models of edits.
Language Anisotropic Cross-Lingual Model Editing
franklear/lime
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On the newly defined cross-lingual model editing task, we empirically demonstrate the failure of monolingual baselines in propagating the edit to multiple languages and the effectiveness of the proposed language anisotropic model editing.
