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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 21–30 of 61 papers

TitleStatusHype
Multi-Task Model Merging via Adaptive Weight DisentanglementCode0
No Train but Gain: Language Arithmetic for training-free Language Adapters enhancementCode0
CALM: Consensus-Aware Localized Merging for Multi-Task LearningCode0
Efficient Model Editing with Task Vector Bases: A Theoretical Framework and Scalable ApproachCode0
Efficient Model Editing with Task-Localized Sparse Fine-tuningCode0
Layer-Aware Task Arithmetic: Disentangling Task-Specific and Instruction-Following Knowledge—0
DuET: Dual Incremental Object Detection via Exemplar-Free Task Arithmetic—0
Bias Vector: Mitigating Biases in Language Models with Task Arithmetic Approach—0
Language and Task Arithmetic with Parameter-Efficient Layers for Zero-Shot Summarization—0
Disentangling Task Interference within Neurons: Model Merging in Alignment with Neuronal Mechanisms—0
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