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
Andrew Pilny, Kelly McAninch, Amanda Slone, Kelsey Moore
{"title":"从人工到机器:评估大型语言模型在内容分析中的功效","authors":"Andrew Pilny, Kelly McAninch, Amanda Slone, Kelsey Moore","doi":"10.1080/08824096.2024.2327547","DOIUrl":null,"url":null,"abstract":"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...","PeriodicalId":47084,"journal":{"name":"Communication Research Reports","volume":"61 1","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"From manual to machine: assessing the efficacy of large language models in content analysis\",\"authors\":\"Andrew Pilny, Kelly McAninch, Amanda Slone, Kelsey Moore\",\"doi\":\"10.1080/08824096.2024.2327547\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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...\",\"PeriodicalId\":47084,\"journal\":{\"name\":\"Communication Research Reports\",\"volume\":\"61 1\",\"pages\":\"\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-03-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Communication Research Reports\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/08824096.2024.2327547\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMMUNICATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communication Research Reports","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/08824096.2024.2327547","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMMUNICATION","Score":null,"Total":0}
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...
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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...
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
2.60
自引率
0.00%
发文量
20
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信