  Recent developments in large language models (LLM) and generative AI have
unleashed the astonishing capabilities of text-to-image generation systems to
synthesize high-quality images that are faithful to a given reference text,
known as a "prompt". These systems have immediately received lots of attention
from researchers, creators, and common users. Despite the plenty of efforts to
improve the generative models, there is limited work on understanding the
information needs of the users of these systems at scale. We conduct the first
comprehensive analysis of large-scale prompt logs collected from multiple
text-to-image generation systems. Our work is analogous to analyzing the query
logs of Web search engines, a line of work that has made critical contributions
to the glory of the Web search industry and research. Compared with Web search
queries, text-to-image prompts are significantly longer, often organized into
special structures that consist of the subject, form, and intent of the
generation tasks and present unique categories of information needs. Users make
more edits within creation sessions, which present remarkable exploratory
patterns. There is also a considerable gap between the user-input prompts and
the captions of the images included in the open training data of the generative
models. Our findings provide concrete implications on how to improve
text-to-image generation systems for creation purposes.
