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..
On Variational Learning of Controllable Representations for Text without Supervision
Authors: Peng Xu, Jackie Chi Kit Cheung, Yanshuai Cao
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, our method outperforms unsupervised baselines and strong supervised approaches on text style transfer, and is capable of performing more flexible fine-grained control over text generation than existing methods. |
| Researcher Affiliation | Collaboration | 1Borealis AI 2Mc Gill University 3Canada CIFAR Chair, Mila. Correspondence to: Peng Xu <EMAIL>. |
| Pseudocode | No | The paper describes the proposed method in prose but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code to reproduce our results can be found in https: //github.com/Borealis AI/CP-VAE |
| Open Datasets | Yes | To perform unsupervised sentiment manipulation, we use the Yelp restaurant reviews dataset and the same data split following Li et al. (2018). |
| Dataset Splits | Yes | To perform unsupervised sentiment manipulation, we use the Yelp restaurant reviews dataset and the same data split following Li et al. (2018). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions software components like 'GPT-2' and 'GloVe embeddings' as models or resources used, but does not specify programming languages, libraries, or solvers with version numbers required for reproducibility. |
| Experiment Setup | Yes | Detailed configurations including the hyperparameters, model architecture, training regimes, and decoding strategy are found in Appendix C. |