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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 110 of 61 papers

TitleStatusHype
Localizing Task Information for Improved Model Merging and CompressionCode2
Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task ArithmeticCode2
Task Singular Vectors: Reducing Task Interference in Model MergingCode2
Editing Models with Task ArithmeticCode2
An Empirical Study of Multimodal Model MergingCode1
Localize-and-Stitch: Efficient Model Merging via Sparse Task ArithmeticCode1
Have You Merged My Model? On The Robustness of Large Language Model IP Protection Methods Against Model MergingCode1
AdaMerging: Adaptive Model Merging for Multi-Task LearningCode1
Concrete Subspace Learning based Interference Elimination for Multi-task Model FusionCode1
Fine-Tuning Attention Modules Only: Enhancing Weight Disentanglement in Task ArithmeticCode1
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