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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 41–50 of 61 papers

TitleStatusHype
On Giant's Shoulders: Effortless Weak to Strong by Dynamic Logits Fusion—0
OpenThaiGPT 1.6 and R1: Thai-Centric Open Source and Reasoning Large Language Models—0
Beyond Task Vectors: Selective Task Arithmetic Based on Importance Metrics—0
Scalable Strategies for Continual Learning with Replay—0
Single-Input Multi-Output Model Merging: Leveraging Foundation Models for Dense Multi-Task Learning—0
Soup to go: mitigating forgetting during continual learning with model averaging—0
Subspace-Boosted Model Merging—0
Task Arithmetic can Mitigate Synthetic-to-Real Gap in Automatic Speech Recognition—0
Task Arithmetic for Language Expansion in Speech Translation—0
BADTV: Unveiling Backdoor Threats in Third-Party Task Vectors—0
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