Generation and Validation of Teaching Examples Based on Large Language Models

Example sentences serve as a crucial bridge for learners to master language application rules, enhance language skills, and develop a sense of language. These sentences encompass various aspects, including semantics, grammar, and pragmatics, and hold significant importance in the fields of language teaching and publishing. Large Language Model (LLM) have facilitated the construction and development of generative corpora. Empowered by LLM, example sentences are linked with linguistic elements such as parts of speech and meanings. During the generation process, both coarse-grained and fine-grained resources are fully utilized; in the screening process, relevant research findings on example sentences, errors, and corrections are extensively referenced to form screening norms. This approach results in the construction of a generative example sentence corpus that meets educational needs and maintains a high degree of standardization.

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Publication Details

Published
2024-08-04
DOI
https://doi.org/10.1109/ialp63756.2024.10661177
Citations
3
Primary Topic
Educational Technology and Assessment
Type
article
Field-Weighted Citation Impact
2.34
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article

Generation and Validation of Teaching Examples Based on Large Language Models

Qing He, Yu Wang, Gaoqi Rao
3 citations
Educational Technology and Assessment
2.34
article

Generation and Validation of Teaching Examples Based on Large Language Models

Qing He, Yu Wang, Gaoqi Rao
article en
3 citations

Abstract

Example sentences serve as a crucial bridge for learners to master language application rules, enhance language skills, and develop a sense of language. These sentences encompass various aspects, including semantics, grammar, and pragmatics, and hold significant importance in the fields of language teaching and publishing. Large Language Model (LLM) have facilitated the construction and development of generative corpora. Empowered by LLM, example sentences are linked with linguistic elements such as parts of speech and meanings. During the generation process, both coarse-grained and fine-grained resources are fully utilized; in the screening process, relevant research findings on example sentences, errors, and corrections are extensively referenced to form screening norms. This approach results in the construction of a generative example sentence corpus that meets educational needs and maintains a high degree of standardization.

Beijing Language and Culture University (CN)
Quality Education
Openalex Percentile: Top 9%
Educational Technology and Assessment
2.34
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