从人工到机器:评估大型语言模型在内容分析中的功效

IF 1.9 Q2 COMMUNICATION Communication Research Reports Pub Date : 2024-03-12 DOI:10.1080/08824096.2024.2327547
Andrew Pilny, Kelly McAninch, Amanda Slone, Kelsey Moore
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引用次数: 0

摘要

本研究比较了大型语言模型(LLM)和人类编码员在预测文本数据中的关系不确定性方面的性能。采用不同的大型语言模型(gpt-4.0-turbo、gpt-3.5-turbo、Cl...
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From manual to machine: assessing the efficacy of large language models in content analysis
This study compares the performance of Large Language Models (LLMs) and human coders in predicting relational uncertainty from textual data. Employing various LLMs (gpt-4.0-turbo, gpt-3.5-turbo, Cl...
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来源期刊
CiteScore
2.60
自引率
0.00%
发文量
20
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