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Knowledge-Driven Distractor Generation for Cloze-Style Multiple Choice Questions

AAAI 2021 · Large language models › Retrieval and knowledge · approach: Explanatory and multiple-choice QA benchmarks · 37 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add the list-wise distractor selector to Probase CSG (ablation)Probase CSG · Test setF1@3: 5.88 -> 9.19Helped
Add the list-wise distractor selector to WordNet CSG (ablation)WordNet CSG · Test setF1@3: 3.14 -> 7.71Helped
Use Probase rather than WordNet as the candidate-set knowledge base (ablation)CSG + DS (list-wise) · Test setF1@3: 7.71 -> 9.19Helped
Remove CSG and use an unstructured candidate set (ablation)Distractor selector · Test setF1@3: 9.19 -> 5.94Hurt
Use a point-wise rather than list-wise ranker (ablation)Probase CSG + DS · Test setF1@3: 9.19 -> 7.91Hurt
Use a pair-wise rather than list-wise ranker (ablation)Probase CSG + DS · Test setF1@3: 9.19 -> 9.42Mixed
Use a point-wise rather than list-wise ranker (ablation)WordNet CSG + DS · Test setF1@3: 7.71 -> 7.26Hurt
Use a pair-wise rather than list-wise ranker (ablation)WordNet CSG + DS · Test setF1@3: 7.71 -> 7.11Hurt
Use DS rather than LR+RF for candidate rankingProbase CSG · Test setF1@3: 7.48 -> 9.19Helped
Use DS rather than LR+LM for candidate rankingProbase CSG · Test setF1@3: 7.62 -> 9.19Helped
Use DS rather than EmbSim+CF for candidate rankingProbase CSG · Test setF1@3: 7.01 -> 9.19Helped
Use edit distance to rank candidatesProbase CSG · Test setF1@3: 5.88 -> 0.82Hurt

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