The remarkable success of large language models (LLMs) has drawn people's great interest in their deployment in specific domains and downstream applications. In this paper, we present the first systematic study of applying large language models (in our case, GPT-3.5 and GPT-4) for the automatic coding (multi-class classification) problem in market research. Our experimental results show that large language models could achieve a macro F1 score of over 0.5 for all our collected real-world market research datasets in a zero-shot setting. We also provide in-depth analyses of the errors made by the large language models. We hope this study sheds light on the lessons we learn and the open challenges large language models have when adapting to a specific market research domain.

Information retrieval has traditionally been framed in terms of searching and extracting information from mostly static resources. Interactive information retrieval (IIR) has widened the scope, with interactive dialogues largely playing the role of clarifying (i.e., making explicit, and/or refining) the information search space. Informed by market research practices, we seek to reframe IIR as a process of eliciting novel information from human interlocutors, with a chatbot-inspired virtual agent playing the role of an interviewer. This reframing flips conventional IIR into what we call an inverse information seeking dialogue, wherein the virtual agent recurrently extracts information from human utterances and poses questions intended to elicit related information. In this work, we introduce and provide a formal definition of an inverse information seeking agent, outline some of its unique challenges, and propose our novel framework to tackle this problem based on techniques from natural language processing (NLP) and IIR.