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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 5161 of 61 papers

TitleStatusHype
Task Arithmetic can Mitigate Synthetic-to-Real Gap in Automatic Speech Recognition0
Task Arithmetic for Language Expansion in Speech Translation0
Leveraging Submodule Linearity Enhances Task Arithmetic Performance in LLMsCode0
CALM: Consensus-Aware Localized Merging for Multi-Task LearningCode0
Investigating Task Arithmetic for Zero-Shot Information RetrievalCode0
Multi-Task Model Merging via Adaptive Weight DisentanglementCode0
Efficient Model Editing with Task Vector Bases: A Theoretical Framework and Scalable ApproachCode0
Efficient Model Editing with Task-Localized Sparse Fine-tuningCode0
No Train but Gain: Language Arithmetic for training-free Language Adapters enhancementCode0
Cross-Model Transfer of Task Vectors via Few-Shot Orthogonal AlignmentCode0
Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge IntegrationCode0
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