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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 2130 of 61 papers

TitleStatusHype
CALM: Consensus-Aware Localized Merging for Multi-Task LearningCode0
FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA0
On Fairness of Task Arithmetic: The Role of Task Vectors0
Scalable Strategies for Continual Learning with Replay0
Cross-Model Transfer of Task Vectors via Few-Shot Orthogonal AlignmentCode0
MCU: Improving Machine Unlearning through Mode Connectivity0
CAT Merging: A Training-Free Approach for Resolving Conflicts in Model Merging0
Investigating Task Arithmetic for Zero-Shot Information RetrievalCode0
Leveraging Submodule Linearity Enhances Task Arithmetic Performance in LLMsCode0
Single-Input Multi-Output Model Merging: Leveraging Foundation Models for Dense Multi-Task Learning0
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