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Large language models (LLMs), such as ChatGPT and GPT-4, are gaining wide-spread real world use. Yet, the two LLMs are closed source, and little is known about the LLMs' performance in real-world use cases. In academia, LLM performance is often measured on benchmarks which may have leaked into ChatGPT's and GPT-4's training data. In this paper, we apply and evaluate ChatGPT and GPT-4 for the real-world task of cost-efficient extractive question answering over a text corpus that was published after the two LLMs completed training. More specifically, we extract research challenges for researchers in the field of HCI from the proceedings of the 2023 Conference on Human Factors in Computing Systems (CHI). We critically evaluate the LLMs on this practical task and conclude that the combination of ChatGPT and GPT-4 makes an excellent cost-efficient means for analyzing a text corpus at scale. Cost-efficiency is key for prototyping research ideas and analyzing text corpora from different perspectives, with implications for applying LLMs in academia and practice. For researchers in HCI, we contribute an interactive visualization of 4392 research challenges in over 90 research topics. We share this visualization and the dataset in the spirit of open science.