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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 3140 of 61 papers

TitleStatusHype
Beyond Task Vectors: Selective Task Arithmetic Based on Importance Metrics0
ATM: Improving Model Merging by Alternating Tuning and Merging0
Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging0
The Non-Local Model Merging Problem: Permutation Symmetries and Variance Collapse0
NegMerge: Consensual Weight Negation for Strong Machine UnlearningCode1
What Matters for Model Merging at Scale?0
Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge IntegrationCode0
Task Arithmetic for Language Expansion in Speech Translation0
Localize-and-Stitch: Efficient Model Merging via Sparse Task ArithmeticCode1
Fine-Tuning Attention Modules Only: Enhancing Weight Disentanglement in Task ArithmeticCode1
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