Exploring Automated Keyword Mnemonics Generation with Large Language Models via Overgenerate-and-Rank
<|reference_start|>Exploring Automated Keyword Mnemonics Generation with Large Language Models via Overgenerate-and-Rank: In this paper, we study an under-explored area of language and vocabulary learning: keyword mnemonics, a technique for memorizing vocabulary through memorable associations with a target word via a verbal cue. Typically, creating verbal cues requires extensive human effort and is quite time-consuming, necessitating an automated method that is more scalable. We propose a novel overgenerate-and-rank method via prompting large language models (LLMs) to generate verbal cues and then ranking them according to psycholinguistic measures and takeaways from a pilot user study. To assess cue quality, we conduct both an automated evaluation of imageability and coherence, as well as a human evaluation involving English teachers and learners. Results show that LLM-generated mnemonics are comparable to human-generated ones in terms of imageability, coherence, and perceived usefulness, but there remains plenty of room for improvement due to the diversity in background and preference among language learners.<|reference_end|>
@article{lee2024exploring,
    title={Exploring Automated Keyword Mnemonics Generation with Large Language
  Models via Overgenerate-and-Rank},
    author={Jaewook Lee, Hunter McNichols, Andrew Lan},
    journal={arXiv preprint arXiv:2409.13952},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13952},
    primaryClass={cs.CL cs.HC}
}