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This paper presents an overgenerate-and-rank prompting approach that uses large language models to generate candidate verbal cues and ranks them with psycholinguistic criteria. Automated evaluation uses imageability and coherence metrics, complemented by human judgments from English teachers and learners. Results show comparable performance to human-written mnemonics with room for improvement across learner backgrounds and preferences.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/exploring-automated-keyword-mnemonics-generation-with-large-language-models-via-overgenerate-and-rank-research-and-analysis/272091/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/exploring-automated-keyword-mnemonics-generation-with-large-language-models-via-overgenerate-and-rank-research-and-analysis/272091.png","ImageObject",300,407,{"name":92,"@type":93},"Aladdin","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-15",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",17,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the paper address in keyword mnemonic generation?","Question",{"text":113,"@type":114},"Creating verbal cues for keyword mnemonics is time-consuming and requires careful human effort to find suitable keywords and craft memorable associations. The paper targets scalable automated generation of verbal cues using large language models.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the proposed overgenerate-and-rank method work?",{"text":118,"@type":114},"The method prompts an LLM to generate candidate syllabic keywords and corresponding verbal cues, then overgenerates and ranks the candidates using psycholinguistic measures to select higher-quality options.",{"name":120,"@type":111,"acceptedAnswer":121},"How is mnemonic quality evaluated in the study?",{"text":122,"@type":114},"Quality is assessed with automated evaluation of imageability and coherence, and with human evaluation involving English teachers and learners that measures perceived usefulness.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},272091,1789463171,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},2336478503145,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Exploring Automated Keyword Mnemonics Generation with Large Language Models via Overgenerate-and-Rank  \nJaewook Lee, Hunter McNichols, Andrew Lan  \nUniversity of Massachusetts Amherst {jaewooklee,wmcnichols,[andrewlan}@cs.umass.edu](andrewlan}@cs.umass.edu)  \nAbstract  \nIn 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-andrank 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.  \n1 Introduction  \nRecent advances in natural language processing have expanded the exploration of developing automated methods for applications in language learning, including second language acquisition (Zhang et al., 2021 ; Yeung and Lee, 2021 ; Okano et al., 2023), linguistic skill modeling (Zylich and Lan, 2021), language assessment and correction (Katinskaia and Yangarber, 2021), and language practice through conversational chatbots (Tyen et al., 2022 ; Liang et al., 2023) . In this work, we investigate an intriguing but relatively under-explored vocabulary learning application: keyword mnemonics (KM) (Atkinson and Raugh, 1975) .  \nKM is a widely utilized technique employed by language learners to memorize vocabulary effectively, through the creation of memorable associations with keywords that they already know, aided  \nTarget Word (Keywords)  \nVerbal Cue  \nVisual Cue  \n\n| alleviate (a, leaf, he, ate) |\n| --- |\n| On his plate, there was a leaf he ate to alleviate his hunger. |\n| |\n\nFigure 1: An example of human-authored verbal cue (Geer and Geer, 2018) for the target word “alleviate” and visual cue generated by Stable Diffusion XL. We study automated verbal cue generation in this work and leave visual cue generation for future work.  \nby verbal and visual cues. This technique is used in many language learning resources, from traditional books (Burchers et al., 2000 ; Heisig, 2011 ; Geerand Geer, 2018) to modern community-based learning platforms (Mnemonic Dictionary, 2024 ; Koohii Kanji, 2024), where users contribute mnemonicsand the community vote on their effectiveness.  \nKM involves a two-step process that creates an acoustic and imagery link for the learner. For instance, consider a learner learning the target word“alleviate”, as shown in Figure 1. First, an acoustic link is established to an already-known set of keywords with similar pronunciation, e.g., “a, leaf, he, ate.” Second, a imagery link is established to these keywords with a verbal cue that evokes a mental image, e.g.,“On his plate, there was a leaf to alleviate his hunger.” These links can be further reinforced using a visual cue.  \nDespite its effectiveness, KM generation is challenging since it demands significant human effort to meticulously find keywords that resemble the target word and create memorable verbal cues. For instance, the word “alleviate” has a large number of possible keyword combinations, such as “a,  \n5521  \nFindings of the Association for Computational Linguistics: EMNLP 2024 , pages 5521–5542 November 12-16, 2024 ©2024 Association for Computational Linguistics  \nleaf, he, ate” or “a, levy, it”; sorting through them takes considerable effort from teachers and learners. Moreover, t","cbCaimug6ptBt5J2","https://ap.wps.com/l/cbCaimug6ptBt5J2","pdf",8844211,22,"English","# Abstract\n# 1 Introduction\n## Keyword Mnemonics (KM)\n## Challenges in Automated KM Generation\n## Related Work\n## Evaluation Challenges and Need for Automated Metrics\n# Contributions","[{\"question\":\"What problem does the paper address in keyword mnemonic generation?\",\"answer\":\"Creating verbal cues for keyword mnemonics is time-consuming and requires careful human effort to find suitable keywords and craft memorable associations. The paper targets scalable automated generation of verbal cues using large language models.\"},{\"question\":\"How does the proposed overgenerate-and-rank method work?\",\"answer\":\"The method prompts an LLM to generate candidate syllabic keywords and corresponding verbal cues, then overgenerates and ranks the candidates using psycholinguistic measures to select higher-quality options.\"},{\"question\":\"How is mnemonic quality evaluated in the study?\",\"answer\":\"Quality is assessed with automated evaluation of imageability and coherence, and with human evaluation involving English teachers and learners that measures perceived usefulness.\"}]","Exploring Automated Keyword Mnemonics Generation with Large Language Models via Overgenerate-and-Rank - Research and analysis | PDF",55]