  Labeling data is essential for training text classifiers but is often
difficult to accomplish accurately, especially for complex and abstract
concepts. Seeking an improved method, this paper employs a novel approach using
a generative language model (GPT-4) to produce labels and rationales for
large-scale text analysis. We apply this approach to the task of discovering
public value expressions in US AI patents. We collect a database comprising
154,934 patent documents using an advanced Boolean query submitted to
InnovationQ+. The results are merged with full patent text from the USPTO,
resulting in 5.4 million sentences. We design a framework for identifying and
labeling public value expressions in these AI patent sentences. A prompt for
GPT-4 is developed which includes definitions, guidelines, examples, and
rationales for text classification. We evaluate the quality of the labels and
rationales produced by GPT-4 using BLEU scores and topic modeling and find that
they are accurate, diverse, and faithful. These rationales also serve as a
chain-of-thought for the model, a transparent mechanism for human verification,
and support for human annotators to overcome cognitive limitations. We conclude
that GPT-4 achieved a high-level of recognition of public value theory from our
framework, which it also uses to discover unseen public value expressions. We
use the labels produced by GPT-4 to train BERT-based classifiers and predict
sentences on the entire database, achieving high F1 scores for the 3-class
(0.85) and 2-class classification (0.91) tasks. We discuss the implications of
our approach for conducting large-scale text analyses with complex and abstract
concepts and suggest that, with careful framework design and interactive human
oversight, generative language models can offer significant advantages in
quality and in reduced time and costs for producing labels and rationales.
