Summary of Disgem: Distractor Generation For Multiple Choice Questions with Span Masking, by Devrim Cavusoglu et al.
DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking
by Devrim Cavusoglu, Secil Sen, Ulas Sert
First submitted to arxiv on: 26 Sep 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 proposes a simple, generic framework for generating distractors for multiple-choice questions (MCQs) in natural language processing. The framework uses pre-trained language models and consists of two stages: candidate generation and candidate selection. Unlike previous methods, this approach does not require additional training on specific datasets. Experimental results show that the proposed framework outperforms previous methods without the need for fine-tuning or retraining. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us create better questions for quizzes and tests by using special computer models to come up with tricky answers. It’s a big improvement over what we had before, and it means we don’t have to spend as much time training our computers to do this task. People who tried out the new method thought it worked really well. |
Keywords
» Artificial intelligence » Fine tuning » Natural language processing
