  Large language models (LLMs) that have been trained on a corpus that includes
large amount of code exhibit a remarkable ability to understand HTML code. As
web interfaces are primarily constructed using HTML, we design an in-depth
study to see how LLMs can be used to retrieve and locate important elements for
a user given query (i.e. task description) in a web interface. In contrast with
prior works, which primarily focused on autonomous web navigation, we decompose
the problem as an even atomic operation - Can LLMs identify the important
information in the web page for a user given query? This decomposition enables
us to scrutinize the current capabilities of LLMs and uncover the opportunities
and challenges they present. Our empirical experiments show that while LLMs
exhibit a reasonable level of performance in retrieving important UI elements,
there is still a substantial room for improvement. We hope our investigation
will inspire follow-up works in overcoming the current challenges in this
domain.
