Distractor Generation

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

17 shown of 17 papers with code (41 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• BRAINTEASER: Lateral Thinking Puzzles for Large Language Models
  8 Oct 2023 2 repositories listed Syntology ran 3 of 7 samples · 4 unverified · 3 pointer-only (licence)
  The success of language models has inspired the NLP community to attend to tasks that require implicit and complex reasoning, relying on human-like commonsense mechanisms.
• Distractor generation for multiple-choice questions with predictive prompting and large language models
  30 Jul 2023 2 repositories listed
  We also show the gains of our approach 1 in generating high-quality distractors by comparing it with a zero-shot ChatGPT and a few-shot ChatGPT prompted with static examples.
• Quiz-Style Question Generation for News Stories
  18 Feb 2021 2 repositories listed
  As a first step towards measuring news informedness at a scale, we study the problem of quiz-style multiple-choice question generation, which may be used to survey users about their knowledge of recent news.
• Generating Distractors for Reading Comprehension Questions from Real Examinations
  8 Sep 2018 2 repositories listed
  We investigate the task of distractor generation for multiple choice reading comprehension questions from examinations.
• Wrong Answers Can Also Be Useful: PlausibleQA -- A Large-Scale QA Dataset with Answer Plausibility Scores
  22 Feb 2025 1 repository listed
  As by their design, LLMs prioritize generating correct answers, the value of highly plausible yet incorrect answers (candidate answers) tends to be overlooked.
• DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking
  26 Sep 2024 1 repository listed
  Recent advancements in Natural Language Processing (NLP) have impacted numerous sub-fields such as natural language generation, natural language inference, question answering, and more.
• Chain-of-Exemplar: Enhancing Distractor Generation for Multimodal Educational Question Generation
  16 Aug 2024 1 repository listed
  Multiple-choice questions (MCQs) are important in enhancing concept learning and student engagement for educational purposes.
• DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice Questions
  27 Jun 2024 1 repository listed Syntology ran 1 of 3 samples · 2 unverified · 3 pointer-only (licence)
  High-quality distractors are crucial to both the assessment and pedagogical value of multiple-choice questions (MCQs), where manually crafting ones that anticipate knowledge deficiencies or misconceptions among real…
• Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models
  2 Apr 2024 1 repository listed
  Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and are a reliable format in assessments and practices.
• CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model
  15 Mar 2024 1 repository listed
  Manually designing cloze test consumes enormous time and efforts.
• A Novel Multi-Stage Prompting Approach for Language Agnostic MCQ Generation using GPT
  13 Jan 2024 1 repository listed
  We introduce a multi-stage prompting approach (MSP) for the generation of multiple choice questions (MCQs), harnessing the capabilities of GPT models such as text-davinci-003 and GPT-4, renowned for their excellence…
• EduQG: A Multi-format Multiple Choice Dataset for the Educational Domain
  12 Oct 2022 1 repository listed
  Thus, our versatile dataset can be used for both question and distractor generation, as well as to explore new challenges such as question format conversion.
• BERT-based distractor generation for Swedish reading comprehension questions using a small-scale dataset
  9 Aug 2021 1 repository listed
  An important part when constructing multiple-choice questions (MCQs) for reading comprehension assessment are the distractors, the incorrect but preferably plausible answer options.
• ZmBART: An Unsupervised Cross-lingual Transfer Framework for Language Generation
  3 Jun 2021 1 repository listed Syntology ran 3 of 4 samples · 1 unverified · 4 pointer-only (licence)
  In this framework, we further pre-train mBART sequence-to-sequence denoising auto-encoder model with an auxiliary task using monolingual data of three languages.
• A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies.
  1 Nov 2020 1 repository listed
  In this paper, we investigate the following two limitations for the existing distractor generation (DG) methods.
• A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies
  12 Oct 2020 1 repository listed
  In this paper, we investigate the following two limitations for the existing distractor generation (DG) methods.
• Distractor Generation for Multiple Choice Questions Using Learning to Rank
  1 Jun 2018 1 repository listed
  We investigate how machine learning models, specifically ranking models, can be used to select useful distractors for multiple choice questions.

Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.