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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
Localize-and-Stitch: Efficient Model Merging via Sparse Task ArithmeticCode1
Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained ModelsCode1
AdaMerging: Adaptive Model Merging for Multi-Task LearningCode1
Knowledge Composition using Task Vectors with Learned Anisotropic ScalingCode1
Parameter Efficient Multi-task Model Fusion with Partial LinearizationCode1
Have You Merged My Model? On The Robustness of Large Language Model IP Protection Methods Against Model MergingCode1
DuET: Dual Incremental Object Detection via Exemplar-Free Task Arithmetic0
Bias Vector: Mitigating Biases in Language Models with Task Arithmetic Approach0
Disentangling Task Interference within Neurons: Model Merging in Alignment with Neuronal Mechanisms0
CultureMERT: Continual Pre-Training for Cross-Cultural Music Representation Learning0
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