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..
Latent Relation Language Models
Authors: Hiroaki Hayashi, Zecong Hu, Chenyan Xiong, Graham Neubig7911-7918
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate empirical improvements over both word-based language models and a previous approach that incorporates knowledge graph information. |
| Researcher Affiliation | Collaboration | 1Carnegie Mellon University, 2Microsoft Research AI EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Generative Process of LRLM |
| Open Source Code | Yes | Equal Contribution. Code & Data: https://github.com/neulab/lrlm. |
| Open Datasets | Yes | Wiki Facts (Ahn et al. 2016) is a collection of Wikipedia articles restricted to /film/actor domain entities in Freebase (Bollacker et al. 2008). |
| Dataset Splits | Yes | Since of๏ฌcial splits for evaluation are not provided, we follow previous work and performed a random split of 80/10/10%. |
| Hardware Specification | No | The paper mentions implementing models in Py Torch and training them, but does not provide specific details on the hardware used for experiments, such as GPU or CPU models. |
| Software Dependencies | No | We implement all models in Py Torch (Paszke et al. 2017). |
| Experiment Setup | Yes | Training details and hyperparameters are summarized in Appendix B. |