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Distractor Generation

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Given a passage, a question, and an answer phrase, the goal of distractor generation (DG) is to generate context-related wrong options (i.e., distractor) for multiple-choice questions (MCQ).

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/DataScienceUIBK/PlausibleQA

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Wrong Answers Can Also Be Useful: PlausibleQA -- A Large-Scale QA Dataset with Answer Plausibility Scores

https://github.com/Evan-Gao/Distractor-Generation-RACE

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Generating Distractors for Reading Comprehension Questions from Real Examinations

https://github.com/andychiangsh/cdgp

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CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model

https://github.com/dkalpakchi/swequad-mc

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BERT-based distractor generation for Swedish reading comprehension questions using a small-scale dataset

https://github.com/google-research-datasets/NewsQuizQA

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Quiz-Style Question Generation for News Stories

https://github.com/kaushal0494/ZmBART

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ZmBART: An Unsupervised Cross-lingual Transfer Framework for Language Generation

https://github.com/my625/cot-mcqgen

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A Novel Multi-Stage Prompting Approach for Language Agnostic MCQ Generation using GPT

https://github.com/semerekiros/distractgpt

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Distractor generation for multiple-choice questions with predictive prompting and large language models

https://github.com/umass-ml4ed/divert

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DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice Questions

https://github.com/umass-ml4ed/prompt_distractor_generation_naacl

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Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models

https://github.com/voidful/BDG

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A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.