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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
Task Singular Vectors: Reducing Task Interference in Model MergingCode2
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
Editing Models with Task ArithmeticCode2
NegMerge: Consensual Weight Negation for Strong Machine UnlearningCode1
Localize-and-Stitch: Efficient Model Merging via Sparse Task ArithmeticCode1
Fine-Tuning Attention Modules Only: Enhancing Weight Disentanglement in Task ArithmeticCode1
Knowledge Composition using Task Vectors with Learned Anisotropic ScalingCode1
Have You Merged My Model? On The Robustness of Large Language Model IP Protection Methods Against Model MergingCode1
Merging Multi-Task Models via Weight-Ensembling Mixture of ExpertsCode1
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