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..
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
Authors: Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi8732-8740
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The best state-of-the-art methods on WINOGRANDE achieve 59.4 79.1%, which are 15-35% (absolute) below human performance of 94.0%, depending on the amount of the training data allowed (2% 100% respectively). Furthermore, we establish new state-of-the-art results on five related benchmarks WSC ( 90.1%), DPR ( 93.1%), COPA( 90.6%), Know Ref ( 85.6%), and Winogender ( 97.1%). |
| Researcher Affiliation | Collaboration | Allen Institute for Artificial Intelligence, University of Washington EMAIL |
| Pseudocode | Yes | Algorithm 1: AFLITE |
| Open Source Code | Yes | Our datasets, crowdsourcing interface, and models are available at http://winogrande.allenai.org. |
| Open Datasets | Yes | To investigate this question, we introduce WINOGRANDE, a large-scale dataset of 44k problems... Our datasets, crowdsourcing interface, and models are available at http://winogrande.allenai.org. |
| Dataset Splits | Yes | Concretely, we use 6k instances (5k for training and 1k for validation) from the dataset (containing 53k instances in total) to fine-tune Ro BERTa (referred to as Ro BERTaembed). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using RoBERTa and BERT, and fairseq in a footnote, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Concretely, we use 6k instances (5k for training and 1k for validation) from the dataset (containing 53k instances in total) to fine-tune Ro BERTa (referred to as Ro BERTaembed). ... When applying AFLITE to WINOGRANDE, we set m = 10, 000, n = 64, k = 500, and τ = 0.75. |