
## FactGen: Faithful Text Generation by Factuality-aware Pre-training and Contrastive Ranking Fine-tuning
ZhiBin Lan, Wei Li, Jinsong Su, Xinyan Xiao, Jiachen Liu, Wenhao Wu, Yajuan Lyu
Keywords: 
JAIR/2023/Proceedings/14267 - FactGen: Faithful Text Generation by Factuality-aware Pre-training and Contrastive Ranking Fine-tuning.pdf
Project URL: 

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[8]

The authors state the toolkit they use for their model development in footnote 1. The implementation is not given. Overview in figure 1.

### Data
_Given the data description in the documentation, how much effort take to either: Find the same dataset the authors used, or similar datasets and defend the comparability, or acquire one from scratch?_

[4]

(5/5)

The datasets (RealNews-like, XSum, CNN/DM, WIKIPERSON, NLI) are very briefly described in 4.1, statistics are missing. Citations given. No links.

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for their results, and compare against their budgetary constraints?_

[3]

The authors state the hyperparameter values per dataset in table 3. The architecture is stated in 4.1. and the optimiser settings as well. No acquisition specified.

### Experimental Procedure
_Given the experimental set-up of the work, how difficult is it to set up a new experiment, similar to those presented in the original work, with the same procedure?_

[2]

The authors state the dataset split in table 2, and describe the evaluation metrics (with citation) in 4.2. Results are single model. Its not actually specified on which subset they present the results.

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying on the available documentation?_

[6]

Requries expertise on NLP.
