Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Zero-Shot Task Adaptation with Relevant Feature Information
Authors: Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that the proposed method outperforms existing methods with four real-world datasets. |
| Researcher Affiliation | Industry | Atsutoshi Kumagai1, Tomoharu Iwata2, Yasuhiro Fujiwara2 1 NTT Computer and Data Science Laboratories 2 NTT Communication Science Laboratories EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Meta-training procedure of our model. |
| Open Source Code | No | The paper does not include any explicit statement about releasing source code for the described methodology or provide a link to a code repository. |
| Open Datasets | Yes | We used four real-world datasets: 20News3, Wo S4, URL5 and Mnistr6. [Footnotes provide URLs to public datasets: 3http://qwone.com/~jason/20Newsgroups/, 4https://github.com/kk7nc/HDLTex, 5https://www.kaggle.com/datasets/shawon10/url-classification-dataset-dmoz, 6https://github.com/ghif/mtae] |
| Dataset Splits | Yes | For 20News, we randomly used 10 classes for training, 5 classes for validation, and 5 classes for testing. For Wo S, we randomly used 69 classes for training, 5 classes for validation, and 5 classes for testing. For URL, we randomly used 7 classes for training, 4 classes for validation, and 4 classes for testing. For Mnistr, we randomly used 30 classes for training, 15 classes for validation, and 15 classes for testing. |
| Hardware Specification | Yes | All experiments were conducted on a Linux server with A100 GPU and 2.20Hz Intel Xeon CPU. |
| Software Dependencies | No | All neural network-based methods were implemented using Pytorch (Paszke et al. 2017). This mention of Pytorch does not include a specific version number required for reproducibility. |
| Experiment Setup | Yes | For the proposed method, LR, NN, and LRD2, the number of synthetic unlabeled data N was 50. For the proposed method, LRD2, LRD2U, and NPU, the step size of gradient descent ฮฑ and the iteration number I in the inner problems were selected from {10, 1, 10 1} and {2, 5, 10}, respectively. For the proposed method, regularization parameters ฮป and ยต were selected from {1, 10 1, 10 2, 0} and {10, 1, 10 1, 10 2, 10 3}, respectively. ... For all neural network-based methods, we used the Adam optimizer with a learning rate of 10 3 (Kingma and Ba 2014). |