Supporting Qualitative Analysis With Large Language Models: Combining Codebook With GPT-3 For Deductive Coding

Ziang Xiao, Xingdi Yuan, Q. Vera Liao, Rania Abdelghani, Pierre-yves Oudeyer. IUI '23: 28th International Conference on Intelligent User Interfaces 2023 – 94 citations

[Paper]    
Model Architecture GPT Fine-Tuning Tools Reinforcement Learning Prompting Training Techniques

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 directly for various tasks without fine-tuning through prompt learning. Using a curiosity-driven questions coding task as a case study, we found, by combining GPT-3 with expert-drafted codebooks, our proposed approach achieved fair to substantial agreements with expert-coded results. We lay out challenges and opportunities in using LLMs to support qualitative coding and beyond.

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