Supporting Qualitative Analysis with Large Language Models: Combining Codebook with GPT-3 for Deductive Coding
Microsoft (United States) · Johns Hopkins University · +4 more institutions
Abstract
Qualitative analysis of textual contents unpacks rich and valuable information by assigning labels to the data. However, this process is often labor-intensive, particularly when working with large datasets. While recent AI-based tools demonstrate utility, researchers may not have readily available AI resources and expertise, let alone be challenged by the limited generalizability of those task-specific models. In this study, we explored the use of large language models (LLMs) in supporting deductive coding, a major category of qualitative analysis where researchers use pre-determined codebooks to label the data into a fixed set of codes. Instead of training task-specific models, a pre-trained LLM could be used…
Citation impact
- FWCI
- 33.30
- Percentile
- 100%
- References
- 18
Authors
5- ZXZiang XiaoCorresponding
Microsoft (United States), Johns Hopkins University, Research Canada
- XYXingdi Yuan
Microsoft (Canada), Microsoft Research Montréal (Canada)
- QVQ. Vera Liao
Microsoft (Canada), Microsoft Research Montréal (Canada)
- RARania Abdelghani
Institut national de recherche en sciences et technologies du numérique
- POPierre-Yves Oudeyer
Institut national de recherche en sciences et technologies du numérique
Topics & keywords
- Codebook
- Generalizability theory
- Computer science
- Coding (social sciences)
- Task (project management)
- Natural language processing
- Curiosity
- Qualitative analysis
- Decent work and economic growth