Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Show Me How To Revise: Improving Lexically Constrained Sentence Generation with XLNet
Authors: Xingwei He, Victor O.K. Li12989-12997
AAAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 19,522 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,452 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results have demonstrated that our proposed model performs much better than the previous work in terms of sentence fluency and diversity. |
| Researcher Affiliation | Academia | Xingwei He, Victor O.K. Li Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Constrained Sentence Generation with XLNet |
| Open Source Code | Yes | Our code, pre-trained models and Appendix are available at https://github.com/NLPCode/MCMCXLNet. |
| Open Datasets | Yes | We used One-Billion-Word corpus1 to construct the synthetic dataset. 1http://www.statmt.org/lm-benchmark/ |
| Dataset Splits | Yes | We selected 6M, 0.3M and 1K sentences from One-Billion-Word corpus as the training, validation, and test sets, respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions 'Hugging Face' and 'GPT-2 small (117M)' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The experiment setups for language models and the classifier are shown in the Appendix. We set N to 20. We set K to 50. All MCMC-based models were run for 200 steps. We ran 20 iterations for Bayesian MCMC, TIGS, L-MCMC, and L-MCMC-C. |