[Paper] Training Question Answering Models From Synthetic Data
Photo Credit Preamble “Training Question Answering Models From Synthetic Data” is an NLP paper from Nvidia that I found very interesting. Question and answer(QA) data is expansive to obtain. If we can use the data we have to generate more data, that will be a huge time saver and create a lot of new possibilities. This paper shows some promising results in this direction. Some caveats: We need big models to be able to get decent results. (The paper reported question generation models with the number of parameters from 117M to 8.3B. See the ablation study in the following sections.) Generated QA data is still not at the same level as the real data. (At least 3x+ more synthetic data is needed to reach the same level of accuracy.) There are a lot of contents in this paper, and it can be a bit overwhelming. I wrote down parts of the paper that I think is most relevant in this post, and hopefully, it can be helpful to you as well. ...