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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

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Showing 51–61 of 61 papers

TitleStatusHype
Task Arithmetic in Trust Region: A Training-Free Model Merging Approach to Navigate Knowledge Conflicts—0
Task Arithmetic Through The Lens Of One-Shot Federated Learning—0
Task Arithmetic with LoRA for Continual Learning—0
ATM: Improving Model Merging by Alternating Tuning and Merging—0
The Non-Local Model Merging Problem: Permutation Symmetries and Variance Collapse—0
To Each (Textual Sequence) Its Own: Improving Memorized-Data Unlearning in Large Language Models—0
FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA—0
Ethos: Rectifying Language Models in Orthogonal Parameter Space—0
HPE-CogVLM: Advancing Vision Language Models with a Head Pose Grounding Task—0
When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers—0
Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging—0
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