# DGRC: An Effective Fine-tuning Framework for Distractor Generation in Chinese Multi-choice Reading Comprehension

arXiv: https://arxiv.org/abs/2405.19139 · HTML: https://mlsanity.com/paper/2405.19139/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 32 references that are in the map.
- First posted on arXiv 29 May 2024.

## Builds on (most cited first, 17 of 17)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learning](https://mlsanity.com/paper/2010.15875/) (2020)
- [Learning to Ask: Neural Question Generation for Reading Comprehension](https://mlsanity.com/paper/1705.00106/) (2017)
- [Few-shot Natural Language Generation for Task-Oriented Dialog](https://mlsanity.com/paper/2002.12328/) (2020)
- [Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension](https://mlsanity.com/paper/1904.09679/) (2019)
- [Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answering](https://mlsanity.com/paper/2403.04890/) (2024)
- [Generating Distractors for Reading Comprehension Questions from Real Examinations](https://mlsanity.com/paper/1809.02768/) (2018)
- [A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies](https://mlsanity.com/paper/2010.05384/) (2020)
- [Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation](https://mlsanity.com/paper/2310.08395/) (2023)
- [Better Distractions: Transformer-based Distractor Generation and Multiple Choice Question Filtering](https://mlsanity.com/paper/2010.09598/) (2020)
- [Distractor generation for multiple-choice questions with predictive prompting and large language models](https://mlsanity.com/paper/2307.16338/) (2023)
- [Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension](https://mlsanity.com/paper/1911.08648/) (2019)
- [BERT-based distractor generation for Swedish reading comprehension questions using a small-scale dataset](https://mlsanity.com/paper/2108.03973/) (2021)
- [A Feasibility Study of Answer-Agnostic Question Generation for Education](https://mlsanity.com/paper/2203.08685/) (2022)
- [CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model](https://mlsanity.com/paper/2403.10326/) (2024)

## Abstract (arXiv)

When evaluating a learner's knowledge proficiency, the multiple-choice question is an efficient and widely used format in standardized tests. Nevertheless, generating these questions, particularly plausible distractors (incorrect options), poses a considerable challenge. Generally, the distractor generation can be classified into cloze-style distractor generation (CDG) and natural questions distractor generation (NQDG). In contrast to the CDG, utilizing pre-trained language models (PLMs) for NQDG presents three primary challenges: (1) PLMs are typically trained to generate ``correct'' content, like answers, while rarely trained to generate ``plausible" content, like distractors; (2) PLMs often struggle to produce content that aligns well with specific knowledge and the style of exams; (3) NQDG necessitates the model to produce longer, context-sensitive, and question-relevant distractors. In this study, we introduce a fine-tuning framework named DGRC for NQDG in Chinese multi-choice reading comprehension from authentic examinations. DGRC comprises three major components: hard chain-of-thought, multi-task learning, and generation mask patterns. The experiment results demonstrate that DGRC significantly enhances generation performance, achieving a more than 2.5-fold improvement in BLEU scores.