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October 15, 2025Open Access

DualReward: A Dynamic Reinforcement Learning Framework for Cloze Tests Distractor Generation

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Experiments showed that DualReward improves distractor generation in cloze tests, suggesting adaptive scaling enhances model performance.

DualReward employs a unique dual reward structure that adapts based on performance and confidence.
Consistent improvements were observed over baseline methods, with up to 3.86% gains in P@1 for diverse datasets.
The framework balances learning from human examples while generating novel distractors for testing.
Its benefits were notable across both passage-level and sentence-level cloze test datasets.

Huang et al. (2025) studied this question.

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October 15, 2025Open Access

View Full Paper

Ask AI

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Authors

Experiments showed that DualReward improves distractor generation in cloze tests, suggesting adaptive scaling enhances model performance.

DualReward employs a unique dual reward structure that adapts based on performance and confidence.
Consistent improvements were observed over baseline methods, with up to 3.86% gains in P@1 for diverse datasets.
The framework balances learning from human examples while generating novel distractors for testing.
Its benefits were notable across both passage-level and sentence-level cloze test datasets.

Huang et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark