Editing models with task arithmetic
Authors: Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, Ali Farhadi
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Overall, our experiments with several models, modalities and tasks show that task arithmetic is a simple, efficient and effective way of editing models. |
| Researcher Affiliation | Collaboration | 1University of Washington 2Microsoft Research 3Allen Institute for AI |
| Pseudocode | No | The paper describes its methods in prose and mathematical formulas but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at https://github.com/mlfoundations/task_vectors. |
| Open Datasets | Yes | For image classification, we use CLIP models [78] and task vectors from eight tasks studied by Ilharco et al. [39]; Radford et al. [78], ranging from satellite imagery recognition to classifying traffic signs: Cars [47], DTD [12], Euro SAT [36], GTSRB [87], MNIST [51], RESISC45 [10], SUN397 [101], and SVHN [72]. For the control task, we use Image Net [16]. |
| Dataset Splits | Yes | For all operations, the model weights obtained by applying θnew = θ +λτnew, where the scaling term λ is determined using held-out validation sets. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments, such as specific GPU or CPU models. |
| Software Dependencies | No | The paper mentions software like 'GPT-2 models [77] from Hugging Face transformers library [97]' and refers to 'PyTorch' [75] and 'Adam W optimizer [58; 75]', but it does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We fine-tune for 2000 iterations with a batch size of 128, learning rate 1e-5 and a cosine annealing learning rate schedule with 200 warm-up steps and the Adam W optimizer [58; 75], with weight decay 0.1. |