用大模型自动生成记忆关键词，效果接近人工，但仍有提升空间。
Exploring Automated Keyword Mnemonics Generation with Large Language Models via Overgenerate-and-Rank
- 通过过生成再排序策略，让大模型批量生成记忆线索并筛选优质结果。
- 生成的线索在可想象性、连贯性和实用性上与人工水平相当。
- 适合语言学习工具开发，也适用于个性化教学场景。
本文研究语言与词汇学习中一个未被充分探索的领域：关键词记忆法，即通过口语提示建立与目标词的生动关联以辅助记忆。传统方法依赖大量人工投入，效率低下，亟需更可扩展的自动化方案。我们提出一种基于提示工程的过生成-排序方法，利用大语言模型生成口语提示，并根据心理语言学指标及预研用户研究结果进行排序。为评估提示质量，我们进行了自动化评估（包括意象性与连贯性）和人工评估（由英语教师与学习者参与）。结果显示，大模型生成的记忆线索在可想象性、连贯性和感知有用性方面与人工生成相当，但由于学习者背景与偏好的多样性，仍有较大改进空间。
原文摘要 · Abstract (English)
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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