Papers by Devrim Çavuşoğlu

2 papers
DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking (2024.findings-emnlp)

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Challenge: Multiple-choice cloze tests are a prevalent form of assessment that evaluates students' comprehension and inference abilities.
Approach: They propose a framework for distractor generation using readily available pre-trained language models . human evaluations confirm that their approach produces more effective distractors .
Outcome: The proposed framework outperforms existing methods without training or fine-tuning human evaluations confirm it.
A multi-level multi-label text classification dataset of 19th century Ottoman and Russian literary and critical texts (2024.findings-acl)

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Challenge: a multi-level, multi-label text classification dataset is used to classify over 3000 documents . authors use a classical bag-of-words (BoW) naive Bayes model and three modern LLMs .
Approach: They propose to apply large language models to a multi-level, multi-label text classification dataset . the dataset features literary and critical texts from 19th-century Ottoman Turkish and Russian .
Outcome: The proposed dataset features literary and critical texts from 19th-century Ottoman Turkish and Russian.

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