Exploring Automated Keyword Mnemonics Generation with Large Language Models via Overgenerate-and-Rank
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929509599543296 |
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| author | Lee, Jaewook McNichols, Hunter Lan, Andrew |
| author_facet | Lee, Jaewook McNichols, Hunter Lan, Andrew |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13952 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Automated Keyword Mnemonics Generation with Large Language Models via Overgenerate-and-Rank Lee, Jaewook McNichols, Hunter Lan, Andrew Computation and Language Human-Computer Interaction 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. |
| title | Exploring Automated Keyword Mnemonics Generation with Large Language Models via Overgenerate-and-Rank |
| topic | Computation and Language Human-Computer Interaction |
| url | https://arxiv.org/abs/2409.13952 |