{"title":"Automating Research in Business and Technical Communication: Large Language Models as Qualitative Coders","authors":"Ryan M. Omizo","doi":"10.1177/10506519241239927","DOIUrl":null,"url":null,"abstract":"The emergence of large language models (LLMs) has disrupted approaches to writing in academic and professional contexts. While much interest has revolved around the ability of LLMs to generate coherent and generically responsible texts with minimal effort and the impact that this will have on writing careers and pedagogy, less attention has been paid to how LLMs can aid writing research. Building from previous research, this study explores the utility of AI text generators to facilitate the qualitative coding research of linguistic data. This study benchmarks five LLM prompting strategies to determine the viability of using LLMs as qualitative coding, not writing, assistants, demonstrating that LLMs can be an effective tool for classifying complex rhetorical expressions and can help business and technical communication researchers quickly produce and test their research designs, enabling them to return insights more quickly and with less initial overhead.","PeriodicalId":46414,"journal":{"name":"Journal of Business and Technical Communication","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Business and Technical Communication","FirstCategoryId":"98","ListUrlMain":"https://doi.org/10.1177/10506519241239927","RegionNum":2,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0
Abstract
The emergence of large language models (LLMs) has disrupted approaches to writing in academic and professional contexts. While much interest has revolved around the ability of LLMs to generate coherent and generically responsible texts with minimal effort and the impact that this will have on writing careers and pedagogy, less attention has been paid to how LLMs can aid writing research. Building from previous research, this study explores the utility of AI text generators to facilitate the qualitative coding research of linguistic data. This study benchmarks five LLM prompting strategies to determine the viability of using LLMs as qualitative coding, not writing, assistants, demonstrating that LLMs can be an effective tool for classifying complex rhetorical expressions and can help business and technical communication researchers quickly produce and test their research designs, enabling them to return insights more quickly and with less initial overhead.
期刊介绍:
JBTC is a refereed journal that provides a forum for discussion of communication practices, problems, and trends in business, professional, scientific, and governmental fields. As such, JBTC offers opportunities for bridging dichotomies that have traditionally existed in professional communication journals between business and technical communication and between industrial and academic audiences. Because JBTC is designed to disseminate knowledge that can lead to improved communication practices in both academe and industry, the journal favors research that will inform professional communicators in both sectors. However, articles addressing one sector or the other will also be considered.