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..
Open Rule Induction
Authors: Wanyun Cui, Xingran Chen
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted extensive experiments to verify the quality and quantity of the inducted open rules. |
| Researcher Affiliation | Academia | Wanyun Cui , Xingran Chen Shanghai University of Finance and Economics EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Supported beam search |
| Open Source Code | Yes | Code and datasets are available at https://github.com/chenxran/Orion |
| Open Datasets | Yes | Code and datasets are available at https://github.com/chenxran/Orion |
| Dataset Splits | No | The paper mentions using several relation extraction datasets (Google-RE, TREx, NYT10, WIKI80, Few Rel, Sem Eval) and their own 'Open Rule155' dataset. However, it does not explicitly provide specific training/validation/test split percentages or sample counts for any of these datasets in the main text that would allow reproduction of data partitioning. |
| Hardware Specification | Yes | All the experiments run over a cloud of servers. Each server has 4 Nvdia Tesla V100 GPUs. |
| Software Dependencies | No | The paper mentions using specific software components like 'Bart', 'Spacy NER library', and 'Exp BERT', but it does not provide specific version numbers for any of these software dependencies. |
| Experiment Setup | Yes | For each converted premise atom, we use Orion to induct k = 5, 10, 20 corresponding open rules. We following the settings of Exp BERT and use k = 29, 41 hypothesis atoms inducted by Orion as for Disease and Spouse, respectively. In addition, we modified Exp BERT to allow the training process to fine-tune the parameters that were frozen in the original Exp BERT, as we found that this will improve the model s effectiveness. |