Exploring Automated Keyword Mnemonics Generation with Large Language Models via Overgenerate-and-Rank

• URL: http://arxiv.org/abs/2409.13952v1
• Date: Sat, 21 Sep 2024 00:00:18 GMT
• Title: Exploring Automated Keyword Mnemonics Generation with Large Language Models via Overgenerate-and-Rank
• Authors: Jaewook Lee, Hunter McNichols, Andrew Lan,
• Abstract summary: Keywords mnemonics are a technique for memorizing vocabulary through memorable associations with a target word via a verbal cue. We propose a novel overgenerate-and-rank method via prompting large language models to generate verbal cues. Results show that LLM-generated mnemonics are comparable to human-generated ones in terms of imageability, coherence, and perceived usefulness.
• Score: 4.383205675898942
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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