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..
KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning
Authors: Ye Liu, Yao Wan, Lifang He, Hao Peng, Philip S. Yu6418-6425
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on benchmark Common Gen dataset verify the effectiveness of our proposed approach by comparing with several strong pre-trained language generation models |
| Researcher Affiliation | Academia | 1University of Illinois at Chicago, Chicago, IL, USA 2Huazhong University of Science and Technology, Wuhan, China 3 Lehigh University, Bethlehem, PA, USA 4Beihang University, Beijing, China |
| Pseudocode | No | The paper describes methods using textual descriptions and diagrams (Figures 2, 3, 4), but does not contain a formal pseudocode or algorithm block. |
| Open Source Code | Yes | 1Our code is available at https://github.com/yeliu918/KG-BART |
| Open Datasets | Yes | Dataset Common Gen (Lin et al. 2020) is a constrained text generation task, which is to explicitly test the ability of machines on commonsense reasoning when generating a text. The dataset released in this task is constructed through a combination of crowdsourced and existing caption corpora, which consists of 77k commonsense descriptions over 35k unique concept sets. |
| Dataset Splits | Yes | Train Dev Test # Concept sets 32,651 993 1,497 # Sentences 67,389 4,018 6,042 |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper mentions various models and techniques (e.g., GPT-2, Uni LM, T5, BART, RoBERTa, TransE, GloVe, CNN) but does not provide specific version numbers for software dependencies or libraries required for replication. |
| Experiment Setup | No | The paper describes the model architecture and pre-training objectives, but does not explicitly state specific hyperparameters (e.g., learning rate, batch size, number of epochs) or detailed training configurations used in their experiments. |