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Table · dataset · 2026

Data and code for 'When Retrieval Can Hurt' (Arabic MCQ generation, 2×2 human evaluation)

Listed in figshare

<p dir="ltr">This deposit holds the research data and analysis code behind a study on automatically writing Arabic multiple-choice questions with large language models.

Description

The work tests whether adding retrieved reference passages and an automatic self-revision step improves question quality, using a four-condition experiment across 31 driving-education topics judged by a panel of raters in three stakeholder roles.</p><p><br></p><p dir="ltr">Included here are the model-produced questions for every condition, the panel's anonymised scores (raters labelled by role only), the computed text-similarity and audit measures, and the scripts that run the statistics and build the figures.

Retrieved passages appear as file-and-page references rather than as text, and the copyrighted source lessons are not shared; only generated items and derived results are provided.</p>

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Where it is published

Catalogue records · 1

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Text 75%
Provenance · 1 source records, 18 field assertions
SourceKeyLast seenRaw
figshareoai:figshare.com:article/3394090311 d agoJSON v1
FieldAssertionExtractorEvidence
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concepts[field].local:field:computer-science-aimapping · figshare comconnector:figshare_com@1.0.0
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concepts[field].local:field:social-sciencemapping · figshare comconnector:figshare_com@1.0.0
concepts[modality].local:modality:textenrichment · figshare comkeyword-concept-rules@1.0.0title+description (75%)
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