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Task Arithmetic

A task vector specifies a direction in the weight space of a pre-trained model, such that movement in that direction improves performance on the task. We build task vectors by subtracting the weights of a pre-trained model from the weights of the same model after fine-tuning on a task. We show that these task vectors can be modified and combined together through arithmetic operations such as negation and addition, and the behavior of the resulting model is steered accordingly.

Papers

Showing 1120 of 61 papers

TitleStatusHype
AdaMerging: Adaptive Model Merging for Multi-Task LearningCode1
Localize-and-Stitch: Efficient Model Merging via Sparse Task ArithmeticCode1
Merging Multi-Task Models via Weight-Ensembling Mixture of ExpertsCode1
NegMerge: Consensual Weight Negation for Strong Machine UnlearningCode1
Parameter Efficient Multi-task Model Fusion with Partial LinearizationCode1
Fine-Tuning Attention Modules Only: Enhancing Weight Disentanglement in Task ArithmeticCode1
Multi-Task Model Merging via Adaptive Weight DisentanglementCode0
Cross-Model Transfer of Task Vectors via Few-Shot Orthogonal AlignmentCode0
CALM: Consensus-Aware Localized Merging for Multi-Task LearningCode0
Efficient Model Editing with Task Vector Bases: A Theoretical Framework and Scalable ApproachCode0
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