Summary of Qgeval: Benchmarking Multi-dimensional Evaluation For Question Generation, by Weiping Fu et al.
QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation
by Weiping Fu, Bifan Wei, Jianxiang Hu, Zhongmin Cai, Jun Liu
First submitted to arxiv on: 9 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed QGEval benchmark evaluates question generation models and automatic metrics across seven dimensions: fluency, clarity, conciseness, relevance, consistency, answerability, and answer consistency. This comprehensive approach aims to provide a unified framework for assessing the quality of generated questions and existing metrics. By examining correlations and distinctions between these dimensions, researchers can better understand the strengths and weaknesses of various QG models and metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary QGEval is a new way to test how well computer programs create good questions. Right now, people use different methods to judge how good these questions are. But this makes it hard to compare the results from different programs. To fix this problem, we created QGEval, which looks at seven important things: how clear and easy to read the questions are, how relevant they are to what’s being asked, and more. We showed that using QGEval helps us see what these programs are good or bad at. |
