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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 31–40 of 61 papers

TitleStatusHype
CultureMERT: Continual Pre-Training for Cross-Cultural Music Representation Learning—0
Mediator: Memory-efficient LLM Merging with Less Parameter Conflicts and Uncertainty Based Routing—0
CAT Merging: A Training-Free Approach for Resolving Conflicts in Model Merging—0
MetaGPT: Merging Large Language Models Using Model Exclusive Task Arithmetic—0
MCU: Improving Machine Unlearning through Mode Connectivity—0
What Matters for Model Merging at Scale?—0
Bias Vector: Mitigating Biases in Language Models with Task Arithmetic Approach—0
Neural Networks Remember More: The Power of Parameter Isolation and Combination—0
When Domain Generalization meets Generalized Category Discovery: An Adaptive Task-Arithmetic Driven Approach—0
On Fairness of Task Arithmetic: The Role of Task Vectors—0
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