  Transforming unstructured text into structured and meaningful forms,
organized by useful category labels, is a fundamental step in text mining for
downstream analysis and application. However, most existing methods for
producing label taxonomies and building text-based label classifiers still rely
heavily on domain expertise and manual curation, making the process expensive
and time-consuming. This is particularly challenging when the label space is
under-specified and large-scale data annotations are unavailable. In this
paper, we address these challenges with Large Language Models (LLMs), whose
prompt-based interface facilitates the induction and use of large-scale pseudo
labels. We propose TnT-LLM, a two-phase framework that employs LLMs to automate
the process of end-to-end label generation and assignment with minimal human
effort for any given use-case. In the first phase, we introduce a zero-shot,
multi-stage reasoning approach which enables LLMs to produce and refine a label
taxonomy iteratively. In the second phase, LLMs are used as data labelers that
yield training samples so that lightweight supervised classifiers can be
reliably built, deployed, and served at scale. We apply TnT-LLM to the analysis
of user intent and conversational domain for Bing Copilot (formerly Bing Chat),
an open-domain chat-based search engine. Extensive experiments using both human
and automatic evaluation metrics demonstrate that TnT-LLM generates more
accurate and relevant label taxonomies when compared against state-of-the-art
baselines, and achieves a favorable balance between accuracy and efficiency for
classification at scale. We also share our practical experiences and insights
on the challenges and opportunities of using LLMs for large-scale text mining
in real-world applications.
