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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 21–30 of 61 papers

TitleStatusHype
Beyond Task Vectors: Selective Task Arithmetic Based on Importance Metrics—0
HPE-CogVLM: Advancing Vision Language Models with a Head Pose Grounding Task—0
BADTV: Unveiling Backdoor Threats in Third-Party Task Vectors—0
FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA—0
Language and Task Arithmetic with Parameter-Efficient Layers for Zero-Shot Summarization—0
MCU: Improving Machine Unlearning through Mode Connectivity—0
Layer-Aware Task Arithmetic: Disentangling Task-Specific and Instruction-Following Knowledge—0
DuET: Dual Incremental Object Detection via Exemplar-Free Task Arithmetic—0
Ethos: Rectifying Language Models in Orthogonal Parameter Space—0
CAT Merging: A Training-Free Approach for Resolving Conflicts in Model Merging—0
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