Summary of Synthetic Context Generation For Question Generation, by Naiming Liu et al.
Synthetic Context Generation for Question Generation
by Naiming Liu, Zichao Wang, Richard Baraniuk
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the challenge of question generation (QG), a crucial task in natural language processing that remains difficult to solve despite advances in large language models (LLMs). The authors investigate training QG models using synthetic contexts generated from readily available question-answer pairs, rather than relying on domain-specific datasets. They conduct an extensive study to evaluate the performance of these models and their potential impact on QG research and applications. Their findings reveal that synthetic contexts are essential for QG tasks, fine-tuning smaller language models can achieve better performances, and synthetic context and real context can achieve comparable performances. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make computers answer questions more easily. Right now, it’s hard to get computers to generate good questions because there are many different situations where questions might be asked. One way to make it easier is to use smaller computer models that have been trained on special data that includes background information, a question, and the answer. But getting this kind of data can be tricky. The researchers in this paper explore using synthetic data – fake context made from real question-answer pairs – to train these small computer models. They want to know if using synthetic contexts will make it easier for computers to generate good questions. Their results show that synthetic contexts are important, smaller models can work well, and the way they do things now isn’t much different than using fake data. |
Keywords
» Artificial intelligence » Fine tuning » Natural language processing » Synthetic data
