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..
KEST: Kernel Distance Based Efficient Self-Training for Improving Controllable Text Generation
Authors: Yuxi Feng, Xiaoyuan Yi, Laks V.S. Lakshmanan, Xing Xie
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three controllable generation tasks demonstrate that KEST significantly improves control accuracy while maintaining comparable text fluency and generation diversity against several strong baselines. |
| Researcher Affiliation | Collaboration | 1The University of British Columbia, Vancouver, Canada 2Microsoft Research Asia, Beijing, China |
| Pseudocode | Yes | Algorithm 1 Training Process of KEST |
| Open Source Code | Yes | Code and appendices are available at https://github.com/peterfengyx/KEST. |
| Open Datasets | Yes | We evaluate the sentiment controllability on the IMDb movie review dataset [Maas et al., 2011]. We use the AGNews dataset [Zhang et al., 2015] to evaluate topic controllability. We use the Jigsaw Toxicity Dataset for training... |
| Dataset Splits | Yes | For IMDb, we sample 5% of the training samples as labeled data and directly take their provided unlabeled set. Since there is no separate unlabeled text in AGNews, we sample 3% of training samples as labeled data and use the others as unlabeled ones. For a fair comparison, we keep the ratio of labeled/pseudo/unlabeled text to 1:1:30. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models or types of processors used for experiments. |
| Software Dependencies | No | The paper mentions software like Uni LM, Adam W, RoBERTa-large, BERT-base, and GPT2-XL, but does not specify their version numbers for reproducibility. |
| Experiment Setup | Yes | We use Adam W [Loshchilov and Hutter, 2019] with learning rate = 5e-5, warm-up steps = one epoch, and batch size = 8 for optimization. The top-p (p = 0.9) sampling method is used for decoding in evaluation. We set λc = λag = λnag = 1.0 in Eq. (4) across all tasks. |