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..
AGRO: Adversarial discovery of error-prone Groups for Robust Optimization
Authors: Bhargavi Paranjape, Pradeep Dasigi, Vivek Srikumar, Luke Zettlemoyer, Hannaneh Hajishirzi
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On the WILDS benchmark, AGRO results in 8% higher model performance on average on known worst-groups, compared to prior group discovery approaches used with G-DRO. |
| Researcher Affiliation | Collaboration | Bhargavi Paranjape1 Pradeep Dasigi2 Vivek Srikumar2,3 Luke Zettlemoyer1,4 Hannaneh Hajishirzi1,2 1University of Washington, 2Allen Institute of Artificial Intelligence, 3University of Utah 4Meta AI |
| Pseudocode | Yes | Algorithm 1 presents the pseudo-code for AGRO. |
| Open Source Code | Yes | Our code and models are public3. 3https://github.com/bhargaviparanjape/robust-transformers |
| Open Datasets | Yes | We evaluate performance on four datasets in language and vision from the popular WILDS benchmark (Koh et al., 2021) to study in-the-wild distribution shifts. |
| Dataset Splits | Yes | We follow the train/dev/test split in (Sagawa et al., 2019), which results in 206,175 training examples. |
| Hardware Specification | No | The paper mentions training models on 'GPU' but does not provide specific details such as GPU models, CPU models, or cloud computing instance specifications. |
| Software Dependencies | No | The paper mentions transformer-based models like DeBERTa-Base, BEIT, RoBERTa-base, and ViT, but does not provide specific software versions for libraries, programming languages, or operating systems used in the experiments. |
| Experiment Setup | Yes | Hyperparameters For θ, we use transformer-based (Vaswani et al., 2017) pretrained models... Appendix A.4 and A.6.3 further detail hyperparameter tuning, particularly for AGRO-specific hyperparameters α and m. |