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 [1].
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. |