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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 1–10 of 61 papers

TitleStatusHype
Transferring Visual Explainability of Self-Explaining Models through Task Arithmetic—0
DuET: Dual Incremental Object Detection via Exemplar-Free Task Arithmetic—0
CultureMERT: Continual Pre-Training for Cross-Cultural Music Representation Learning—0
Subspace-Boosted Model Merging—0
CALM: Consensus-Aware Localized Merging for Multi-Task LearningCode0
FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA—0
On Fairness of Task Arithmetic: The Role of Task Vectors—0
Scalable Strategies for Continual Learning with Replay—0
Cross-Model Transfer of Task Vectors via Few-Shot Orthogonal AlignmentCode0
MCU: Improving Machine Unlearning through Mode Connectivity—0
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