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..
Learning Knowledge Graph-based World Models of Textual Environments
Authors: Prithviraj Ammanabrolu, Mark Riedl
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A zero-shot ablation study on neverbefore-seen textual worlds shows that our methodology significantly outperforms existing textual world modeling techniques as well as the importance of each of our contributions. |
| Researcher Affiliation | Academia | Prithviraj Ammanabrolu School of Interactive Computing Georgia Institute of Technology EMAIL Mark O. Riedl School of Interactive Computing Georgia Institute of Technology EMAIL |
| Pseudocode | No | The paper includes architectural diagrams (Figure 2, Figure 3) and mathematical formulations of loss functions, but no explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper links to the Jericho World Dataset (https://github.com/Jericho World/Jericho World) but does not provide a concrete link or explicit statement about the open-sourcing of their Worldformer model's code. |
| Open Datasets | Yes | Dataset. We use the Jericho World Dataset [4].1 1https://github.com/Jericho World/Jericho World |
| Dataset Splits | No | The paper mentions 'training data' and a 'test set' with specific instance counts, but does not explicitly state the existence or size of a validation split or how it was created. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions various software components and models like BERT, GPT-2, ALBERT, Open IE, and Word Net, but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | All sequence models use a fixed graph vocabulary of size 7002... Additional details and hyperparameters for the models are found in Appendix A.2. |