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..
Adversarial Text Generation via Feature-Mover's Distance
Authors: Liqun Chen, Shuyang Dai, Chenyang Tao, Haichao Zhang, Zhe Gan, Dinghan Shen, Yizhe Zhang, Guoyin Wang, Ruiyi Zhang, Lawrence Carin
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on a variety of tasks to evaluate the proposed model empirically, including unconditional text generation, style transfer from non-parallel text, and unsupervised cipher cracking. |
| Researcher Affiliation | Collaboration | 1Duke University, 2Microsoft Dynamics 365 AI Research, 3Microsoft Research, 4Baidu Research |
| Pseudocode | Yes | Algorithm 1 IPOT algorithm [59] and Algorithm 2 Adversarial text generation via FMD. |
| Open Source Code | No | Our code will be released to encourage future research. |
| Open Datasets | Yes | CUB captions [57], MS COCO captions [38], and EMNLP2017 WMT News [24]. For unsupervised decipher task, we adapt the idea of feature mover s distance to the original framework of Cipher GAN and test this modified model on the Brown English text dataset [16] referencing The Brown English-language corpus [30]. |
| Dataset Splits | No | Table 1 lists 'Train' and 'Test' dataset sizes but does not provide details on validation splits or percentages. |
| Hardware Specification | No | The paper does not specify any hardware details such as CPU, GPU models, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers for any libraries, frameworks, or programming languages used. |
| Experiment Setup | Yes | Algorithm 2 lists 'batch size n, learning rate η, maximum number of iterations N' as inputs. Additionally, for conditional tasks, 'λ is a hyperparameter that balances these two terms'. |