Papers by Mohammed I. Thanoon
Fine-Tuning Encoder-Decoder Models with Contrastive Learning for In-Context Distractor Generation (2025.findings-emnlp)
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Elaf Alhazmi, Quan Z. Sheng, Wei Emma Zhang, Mohammed I. Thanoon, Haojie Zhuang, Behnaz Soltani, Munazza Zaib
| Challenge: | Distractors are used to generate plausible but incorrect options for fill-in-the-blank questions . research studies focus on fine-tuning pre-trained models with data augmentation techniques to generate distractors . |
| Approach: | They propose a model that trains the model to recognize essential semantic features necessary to generate distractors. |
| Outcome: | The proposed model outperforms existing models on two public datasets. |