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ZENODO
Preprint . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Preprint . 2025
License: CC BY
Data sources: Datacite
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AI辅助语言学研究

Authors: Sheng, Jinbiao;

AI辅助语言学研究

Abstract

摘要: 本文探讨了人工智能辅助语言学研究的多种方法,特别是提示工程(Prompt Engineering)在大语言模型中的应用。提示工程通过结构化输入和角色扮演等方法,提升了模型的准确性和效用。文章还介绍了AI代码生成、智能学术团队的构建以及大语言模型的本地化应用。通过具体案例,如《上海地方志》的标点和翻译,展示了AI在语言学研究中的潜力和应用前景。 关键词: 提示工程、大语言模型、AI代码生成、智能学术团队、本地化应用、GPT4All、LM Studio,检索增强生成(RAG)

Keywords

Artificial intelligence, FOS: Languages and literature, Linguistics

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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green