Papers by Hunter McNichols
Exploring Automated Keyword Mnemonics Generation with Large Language Models via Overgenerate-and-Rank (2024.findings-emnlp)
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| Challenge: | Typically, creating verbal cues requires extensive human effort and is quite time-consuming. |
| Approach: | They propose a method for overgenerating and ranking verbal cues by prompting large language models to generate them and ranking them according to psycholinguistic measures and takeaways from a pilot user study. |
| Outcome: | The proposed method is comparable to human-generated mnemonics in imageability, coherence, and perceived usefulness, but there remains room for improvement due to the diversity in background and preference among language learners. |
Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models (2024.findings-naacl)
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Wanyong Feng, Jaewook Lee, Hunter McNichols, Alexander Scarlatos, Digory Smith, Simon Woodhead, Nancy Ornelas, Andrew Lan
| Challenge: | Multiple-choice questions (MCQs) are easy to administer and grade . but crafting high-quality distractors remains labor-intensive and limited scalability . |
| Approach: | They propose to automate the generation of distractors in math MCQs by using large language models to generate distractors. |
| Outcome: | The proposed methods can generate valid distractors, but they are less adept at anticipating common errors or misconceptions among real students. |