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

arXiv: https://arxiv.org/abs/2409.13952 · HTML: https://mlsanity.com/paper/2409.13952/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 21 Sep 2024.

## Builds on (most cited first, 10 of 10)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [I Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual Metaphors](https://mlsanity.com/paper/2305.14724/) (2023)
- [Predicting Concreteness and Imageability of Words Within and Across Languages via Word Embeddings](https://mlsanity.com/paper/1807.02903/) (2018)
- [A SMART Mnemonic Sounds like "Glue Tonic": Mixing LLMs with Student Feedback to Make Mnemonic Learning Stick](https://mlsanity.com/paper/2406.15352/) (2024)
- [Composition and Deformance: Measuring Imageability with a Text-to-Image Model](https://mlsanity.com/paper/2306.03168/) (2023)
- [SmartPhone: Exploring Keyword Mnemonic with Auto-generated Verbal and Visual Cues](https://mlsanity.com/paper/2305.10436/) (2023)

## Built on by (most cited first, 4 of 4)

- [The StudyChat Dataset: Analyzing Student Dialogues With ChatGPT in an Artificial Intelligence Course](https://mlsanity.com/paper/2503.07928/) (2025)
- [PhoniTale: Phonologically Grounded Mnemonic Generation for Typologically Distant Language Pairs](https://mlsanity.com/paper/2507.05444/) (2025)
- [Interpretable Mnemonic Generation for Kanji Learning via Expectation-Maximization](https://mlsanity.com/paper/2507.05137/) (2025)
- [WordCraft: Scaffolding the Keyword Method for L2 Vocabulary Learning with Multimodal LLMs](https://mlsanity.com/paper/2602.00762/) (2026)

## Abstract (arXiv)

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.