Summary of Dgrc: An Effective Fine-tuning Framework For Distractor Generation in Chinese Multi-choice Reading Comprehension, by Runfeng Lin et al.
DGRC: An Effective Fine-tuning Framework for Distractor Generation in Chinese Multi-choice Reading Comprehension
by Runfeng Lin, Dacheng Xu, Huijiang Wang, Zebiao Chen, Yating Wang, Shouqiang Liu
First submitted to arxiv on: 29 May 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 In this study, researchers develop a fine-tuning framework named DGRC to generate plausible distractors for multiple-choice questions in Chinese multi-choice reading comprehension exams. The framework consists of three components: hard chain-of-thought, multi-task learning, and generation mask patterns. By leveraging pre-trained language models (PLMs), DGRC addresses the challenges of generating distractors that are coherent, context-sensitive, and relevant to specific knowledge domains. The results show a significant improvement in BLEU scores, demonstrating the effectiveness of the proposed approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create better multiple-choice questions for tests by teaching AI models to generate good wrong answers. This is important because it’s hard to come up with believable incorrect options that make sense in the context of the question. The researchers created a new way to train language models using three key ideas: thinking deeply about what makes sense, learning to do many tasks at once, and creating patterns for generating text. By doing this, they were able to get the AI to generate distractors that are much better than before. |
Keywords
» Artificial intelligence » Bleu » Fine tuning » Mask » Multi task
