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

TL;DR

This paper introduces an automated approach using large language models to generate and rank verbal cues for vocabulary learning, aiming to reduce human effort and improve scalability.

Contribution

It presents a novel overgenerate-and-rank method leveraging LLMs for creating effective keyword mnemonics, evaluated through both automated metrics and human assessments.

Findings

LLM-generated mnemonics are comparable to human ones in imageability and coherence.

Automated ranking improves cue quality based on psycholinguistic measures.

There is potential for further improvement due to learner diversity.

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.