mlsanityData to 13 Oct 2026

SmartPhone: Exploring Keyword Mnemonic with Auto-generated Verbal and Visual Cues

arXiv 2305.10436 ↗First posted 11 May 2023cs.CLcited by 4

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Abstract (arXiv)

In second language vocabulary learning, existing works have primarily focused on either the learning interface or scheduling personalized retrieval practices to maximize memory retention. However, the learning content, i.e., the information presented on flashcards, has mostly remained constant. Keyword mnemonic is a notable learning strategy that relates new vocabulary to existing knowledge by building an acoustic and imagery link using a keyword that sounds alike. Beyond that, producing verbal and visual cues associated with the keyword to facilitate building these links requires a manual process and is not scalable. In this paper, we explore an opportunity to use large language models to automatically generate verbal and visual cues for keyword mnemonics. Our approach, an end-to-end pipeline for auto-generating verbal and visual cues, can automatically generate highly memorable cues. We investigate the effectiveness of our approach via a human participant experiment by comparing it with manually generated cues.

Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Builds on 6 of its 20 references that are in the map. First posted on arXiv 11 May 2023.

Builds on

6 of 20 references.

References · 6

  1. 2020Language Models are Few-Shot Learnerscited by 32,320
  2. 2022Training language models to follow instructions with human feedbackcited by 15,856
  3. 2016Enriching Word Vectors with Subword Informationcited by 1,581
  4. 2020Context-Aware Attentive Knowledge Tracingcited by 173
  5. 2020Exploring Controllable Text Generation Techniquescited by 55
  6. 2016Unbounded Human Learning: Optimal Scheduling for Spaced Repetitioncited by 12

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4 papers.