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Large language models (LLMs), such as ChatGPT and GPT-4, are gainingwide-spread real world use. Yet, these LLMs are closed source, and little isknown about their performance in real-world use cases. In this paper, we applyand evaluate the combination of ChatGPT and GPT-4 for the real-world task ofmining insights from a text corpus in order to identify research challenges inthe field of HCI. We extract 4,392 research challenges in over 100 topics fromthe 2023 CHI conference proceedings and visualize the research challenges forinteractive exploration. We critically evaluate the LLMs on this practical taskand conclude that the combination of ChatGPT and GPT-4 makes an excellentcost-efficient means for analyzing a text corpus at scale. Cost-efficiency iskey for flexibly prototyping research ideas and analyzing text corpora fromdifferent perspectives, with implications for applying LLMs for mining insightsin academia and practice.

Mapping the Challenges of HCI: An Application and Evaluation of ChatGPT and GPT-4 for Mining Insights at Scale