A distractor generator trained via pairwise ranking and direct preference optimization produces wrong options that students select more often than options from GPT-3.5, GPT-4o, and other baselines, and yields a higher item discrimination index in a small human test.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Prediction
A distractor generator trained via pairwise ranking and direct preference optimization produces wrong options that students select more often than options from GPT-3.5, GPT-4o, and other baselines, and yields a higher item discrimination index in a small human test.